Updated on 2024/10/10

Information

 

写真a

 
UCHIDA SEIICHI
 
Organization
Trustee(Vice President) Director
Education and Research Center for Mathematical and Data Science （Joint Appointment）
Data-Driven Innovation Initiative （Joint Appointment）
Research Institute of Superconductor Science and Systems （Joint Appointment）

Graduate School of Systems Life Sciences Department of Systems Life Sciences（Joint Appointment）
School of Engineering Department of Electrical Engineering and Computer Science（Joint Appointment）
Graduate School of Information Science and Electrical Engineering Department of Information Science and Technology（Joint Appointment）
Joint Graduate School of Mathematics for Innovation （Joint Appointment）
School of Interdisciplinary Science and Innovation Department of Interdisciplinary Science and Innovation（Joint Appointment）
Title
Director
Contact information
メールアドレス
Profile
ｃ1967 : born in Kitakyushu City, Fukuoka, Japan. 1992 : received M.E. degree from Kyushu University (Fukuoka, Japan) 1992-1996 : joined in SECOM Co., Ltd., Tokyo, Japan. 1999 : received Dr.Eng. degree from Grad. School of Information Science and Electrical Engineering, Kyushu University. 1999-2002 : Research Associate of Faculty of Information Science and Electrical Engineering, Kyushu University 2002-2007: Associate Professor of Faculty of Information Science and Electrical Engineering, Kyushu University 2007-now: Professor of Faculty of Information Science and Electrical Engineering, Kyushu University 2017-now: Distinguished Professor of Kyushu University 2017-now: Director of Education and Research Center for Mathematical and Data Science 2022-now: Senior Vice President of Kyushu University
Homepage
External link

Research Areas

  • Informatics / Human interface and interaction

Degree

  • Dr. Eng.

Research History

  • Kyushu University Senior Vice President

    2022.10 - Present

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  • 九州大学 数理・データサイエンス教育研究センター長

    2017.10 - Present

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  • 九州大学 大学院 システム情報科学研究院 教授

    2007.10 - Present

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  • セコム株式会社IS研究所 1992.3-1996.3

    セコム株式会社IS研究所 1992.3-1996.3

Research Interests・Research Keywords

  • Research theme：動的計画法

    Keyword：動的計画法

    Research period： 2024

  • Research theme：マッチング

    Keyword：マッチング

    Research period： 2024

  • Research theme：font

    Keyword：font

    Research period： 2024

  • Research theme：パターン認識

    Keyword：パターン認識

    Research period： 2024

  • Research theme：パターン照合

    Keyword：パターン照合

    Research period： 2024

  • Research theme：bioimage informatics

    Keyword：bioimage informatics

    Research period： 2024

  • Research theme：OCR

    Keyword：OCR

    Research period： 2024

  • Research theme：glyph

    Keyword：glyph

    Research period： 2024

  • Research theme：画像情報学

    Keyword：画像情報学

    Research period： 2024

  • Research theme：画像処理

    Keyword：画像処理

    Research period： 2024

  • Research theme：深層学習

    Keyword：深層学習

    Research period： 2024

  • Research theme：機械学習

    Keyword：機械学習

    Research period： 2024

  • Research theme：最適化

    Keyword：最適化

    Research period： 2024

  • Research theme：sequential pattern analysis

    Keyword：sequential pattern analysis

    Research period： 2024

  • Research theme：文字認識

    Keyword：文字認識

    Research period： 2024

  • Research theme：practical data analysis (interdisciplinary research)

    Keyword：medical image analysis, visual design analytics, sport performance analysis, digital humanity, infrastructure data analysis,

    Research period： 2017.5

  • Research theme：bioimage informatics

    Keyword：intercellular image processing, multiple object tracking, 3D reconstruction, image segmentation, target detection and counting

    Research period： 2009.10

  • Research theme：Recognition, understanding, and analysis of character patterns

    Keyword：Optical character recognition, font design analysis, handwritten character, handwritings, scene text detection and recognition, document image processing, online character recognition

    Research period： 1999.4

  • Research theme：Recognition, understanding, and analysis of sequential patterns

    Keyword：gesture recognition, activity recognition, video image processing, nonlinear time warping, flow analysis, anomaly detection tracking, early recognition, temporal prediction, video surveillance, dynamic programming

    Research period： 1999.4

  • Research theme：image informatics, pattern recognition, machine learning applicaiton

    Keyword：image analysis, image recognition, image generation, image transformation, deep neural networks, machine learning, anomaly detection

    Research period： 1996.4

Awards

  • 令和5年度九州大学共同研究等活動表彰

    2023.11   九州大学   共同研究等の活性化への貢献が特に顕著であり、多大な貢献をされた功績をたたえ表彰

  • MIRU2023インタラクティブ発表賞

    2023.8   画像の認識・理解シンポジウム実行委員会・プログラム委員会   中鶴慧, 内田誠一 "機械学習によるカーニング" に対する受賞

  • 令和4年度九州大学共同研究等活動表彰

    2022.11   九州大学   共同研究等の活性化への貢献が特に顕著であり、多大な貢献をされた功績をたたえ表彰

  • 第15回 日本統計学会出版賞

    2022.5   日本統計学会   北川 源四郎，竹村 彰通 編， 内田誠一，川崎能典，孝忠大輔，佐久間 淳，椎名 洋，中川裕志，樋口知之，丸山 宏 著 「教養としてのデータサイエンス(データサイエンス入門シリーズ)」(講談社,2021年)

  • 園芸学会年間優秀論文賞

    2022.3   園芸学会   Kanae Masuda, Maria Suzuki, Kohei Baba, Kouki Takeshita, Tetsuya Suzuki, Mayu Sugiura, Takeshi Niikawa, Seiichi Uchida, Takashi Akagi Noninvasive Diagnosis of Seedless Fruit with Deep Learning in Persimmon The Horticulture Journal, vol.90, no.2, pp.172-180, Jan. 2021

  • 令和３年度九州大学共同研究等活動表彰

    2021.11   九州大学   共同研究等の活性化への貢献が特に顕著であり、多大な貢献をされた功績をたたえ表彰

  • MIRU2020インタラクティブ発表賞

    2020.8   画像の認識・理解シンポジウム実行委員会・プログラム委員会   論文発表 "識別・生成のハイブリッドモデルと弱教師あり学習への応用"(早志英朗, 内田誠一)に対する授賞．同シンポジウムは，画像認識における国内最高峰の学術集会．

  • MIRU2019インタラクティブ発表賞

    2019.8   画像の認識・理解シンポジウム実行委員会・プログラム委員会   論文発表"画像に基づく言語変換"(馬場 康平, Brian Kenji Iwana, 内田 誠一) に対する授賞．同シンポジウムは，画像認識における国内最高峰の学術集会．

  • 平成30年度九州大学工学講義賞

    2019.8   九州大学工学部  

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    工学部専攻教育科目「パターン認識」に対する表彰．

  • 平成31年度科学技術分野の文部科学大臣表彰科学技術賞（研究部門）

    2019.4   文部科学省   文字パターンに関する包括的研究

  • フェロー称号付与

    2019.3   電子情報通信学会   「画像および時系列パターンの認識・解析技術の開発とその多分野応用」に関する．なお同学会は，情報系における国内最大学会．

  • 電子情報通信学会 フェロー称号付与

    2019.3   電子情報通信学会   画像および時系列パターンの認識・解析技術の開発とその多分野応用

  • MIRU2017インタラクティブ発表賞

    2017.8   画像の認識・理解シンポジウム実行委員会・プログラム委員会   論文発表"Globally Optimal Object Tracking with Fully Convolutional Networks"(Jinho Lee and Seiichi Uchida) に対する授賞．同シンポジウムは，画像認識における国内最高峰の学術集会．

  • データサイエンスアワード2016

    2016.10   データサイエンティスト協会   バイオイメージ・インフォマティクス：生物学と画像情報学のデータサイエンス協働

  • 平成27年度科研費審査委員表彰

    2015.10   日本学術振興会  

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    科研費の第２段審査（合議審査）に有意義な審査意見を付した第１段審査（書面審査）委員を選考し表彰

  • 電子情報通信学会 情報・システムソサイエティ活動功労賞

    2014.6   電子情報通信学会 情報・システムソサイエティ   ISS 英文論文誌編集委員としての貢献に対する受賞

  • 電子情報通信学会 情報・システムソサイエティ活動功労賞

    2014.6   電子情報通信学会   ISS 英文論文誌編集委員としての貢献

  • Top Reviewer for Pattern Recognition Letters -- 2008-2012

    2013.10   the journal Pattern Recognition Letters  

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    学術雑誌"Pattern Recognition Letters"の2008-2012年の査読委員の中で，最もクオリティが高い査読を行ったもの26名を表彰

  • 九州大学研究活動表彰

    2012.11   九州大学  

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    研究又は産学官連携活動に関し九州大学の研究の活性化と財務上の貢献が特に顕著だったことに対する受賞

  • Best Invited Session Award

    2011.9   KES2011 (15th Annual Conference on Knowledge-Based and Intelligent Information & Engineering Systems)  

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    同会議におけるInvited Session "Document Analysis and Knowledge Science"の企画＆開催に対する授賞

  • MIRU2011優秀論文賞

    2011.7   画像の認識・理解シンポジウム実行委員会・プログラム委員会  

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    論文発表「非マルコフ的制約を導入した最適弾性マッチング」に対する授賞．同シンポジウムは，画像認識における国内最高峰の学術集会．

  • Best Paper Award

    2010.11   12th International Conference on Frontiers in Handwriting Recognition  

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    第12回手書きパターン認識に関する国際会議（The 12th International Conference on Frontiers in Handwriting Recognition，ICFHR2010）において発表した次の論文に対する最優秀論文賞： Kazumasa Iwata, Koichi Kise, Masakazu Iwamura, Seiichi Uchida and Shinichiro Omachi, Tracking and Retrieval of Pen Tip Positions for an Intelligent Camera Pen

  • MPR2010 Best Poster Award

    2010.10   MPR2010 Organizing Committee  

  • 平成22年度電子情報通信学会情報・システムソサイエティ査読功労賞

    2010.3   電子情報通信学会  

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    電子情報通信学会情報・システムソサイエティ論文誌の編集活動における査読者としての顕著な業績

  • 平成20年度電子情報通信学会論文賞

    2009.3   電子情報通信学会   筆順変動を表現するHMMとそのオンライン文字認識への応用

  • MPR2008 Best Poster Award

    2008.11   MPR2008 Organizing Committee  

  • IAPR/ICDAR Best Paper Award

    2007.9   International Association for Pattern Recognition  

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    第９回文書解析と認識に関する国際会議（The 9th International Conference on Document Analysis and Recognition，ICDAR2007）において発表した次の論文に対する最優秀論文賞： Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise, Extraction of Embedded Class Information from Universal Character Pattern

  • MIRU長尾賞（最優秀論文賞)

    2006.7   電子情報通信学会パターン認識・メディア理解研究会  

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    1次元パターンの解析的DPマッチング

  • MIRU2005 インタラクティブセッション優秀賞

    2005.7   情報処理学会CVIM研究会   情報付加による認識率100%の実現 − 人にも機械にも理解可能な情報伝達のために −

  • 平成14年度電気学会論文発表賞Ｂ

    2003.5   電気学会  

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    制約を緩めた弾性マッチングにおける固有変形利用の効果

  • 2003年度電子情報通信学会PRMU研究奨励賞

    2003.5   電子情報通信学会  

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    カテゴリ毎の変形特性を組み込んだ弾性マッチングによる手書き文字認識

  • 2001年度電子情報通信学会九州支部長賞

    2002.3   電子情報通信学会九州支部  

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    手書き文字の変形特性の抽出と利用,

  • 1999年度情報処理学会九州支部奨励賞

    2000.3   情報処理学会九州支部  

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    区分線形2次元ワープを用いた画像中からの物体検出

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Papers

  • Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity

    Kadota T., Hayashi H., Bise R., Tanaka K., Uchida S.

    Medical Image Analysis   97   103262   2024.10   （ ISSN:13618415 ）

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    Language：English   Publisher：Medical Image Analysis  

    Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation cost is high. In contrast, relative annotation, in which the severity between a pair of images is compared, can avoid quantizing severity and thus makes it easier. We can estimate relative disease severity using a learning-to-rank framework with relative annotations, but relative annotation has the problem of the enormous number of pairs that can be annotated. Therefore, the selection of appropriate pairs is essential for relative annotation. In this paper, we propose a deep Bayesian active learning-to-rank that automatically selects appropriate pairs for relative annotation. Our method preferentially annotates unlabeled pairs with high learning efficiency from the model uncertainty of the samples. We prove the theoretical basis for adapting Bayesian neural networks to pairwise learning-to-rank and demonstrate the efficiency of our method through experiments on endoscopic images of ulcerative colitis on both private and public datasets. We also show that our method achieves a high performance under conditions of significant class imbalance because it automatically selects samples from the minority classes.

    DOI： 10.1016/j.media.2024.103262

    Scopus

    PubMed

  • Facile Generation of Heterotelechelic Poly(2-Oxazoline)s Towards Accelerated Exploration of Poly(2-Oxazoline)-Based Nanomedicine

    Van Guyse, JFR; Abbasi, S; Toh, K; Nagorna, Z; Li, JJ; Dirisala, A; Quader, S; Uchida, S; Kataoka, K

    ANGEWANDTE CHEMIE-INTERNATIONAL EDITION   63 ( 27 )   e202404972   2024.7   （ ISSN:1433-7851 eISSN:1521-3773 ）

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  • Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence

    Miyama, K; Akiyama, T; Bise, R; Nakamura, S; Nakashima, Y; Uchida, S

    KNEE   48   128 - 137   2024.6   （ ISSN:0968-0160 eISSN:1873-5800 ）

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    Language：English   Publisher：Knee  

    Background: This study proposed an automatic surgical planning system for high tibial osteotomy (HTO) using deep learning-based artificial intelligence and validated its accuracy. The system simulates osteotomy and measures lower-limb alignment parameters in pre- and post-osteotomy simulations. Methods: A total of 107 whole-leg standing radiographs were obtained from 107 patients who underwent HTO. First, the system detected anatomical landmarks on radiographs. Then, it simulated osteotomy and automatically measured five parameters in pre- and post-osteotomy simulation (hip knee angle [HKA], weight-bearing line ratio [WBL ratio], mechanical lateral distal femoral angle [mLDFA], mechanical medial proximal tibial angle [mMPTA], and mechanical lateral distal tibial angle [mLDTA]). The accuracy of the measured parameters was validated by comparing them with the ground truth (GT) values given by two orthopaedic surgeons. Results: All absolute errors of the system were within 1.5° or 1.5%. All inter-rater correlation confidence (ICC) values between the system and GT showed good reliability (>0.80). Excellent reliability was observed in the HKA (0.99) and WBL ratios (>0.99) for the pre-osteotomy simulation. The intra-rater difference of the system exhibited excellent reliability with an ICC value of 1.00 for all lower-limb alignment parameters in pre- and post-osteotomy simulations. In addition, the measurement time per radiograph (0.24 s) was considerably shorter than that of an orthopaedic surgeon (118 s). Conclusion: The proposed system is practically applicable because it can measure lower-limb alignment parameters accurately and quickly in pre- and post-osteotomy simulations. The system has potential applications in surgical planning systems.

    DOI： 10.1016/j.knee.2024.03.008

    Web of Science

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  • Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell states

    Tomimatsu, K; Fujii, T; Bise, R; Hosoda, K; Taniguchi, Y; Ochiai, H; Ohishi, H; Ando, K; Minami, R; Tanaka, K; Tachibana, T; Mori, S; Harada, A; Maehara, K; Nagasaki, M; Uchida, S; Kimura, H; Narita, M; Ohkawa, Y

    NATURE COMMUNICATIONS   15 ( 1 )   3657   2024.5   （ eISSN:2041-1723 ）

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    Language：English   Publisher：Nature Communications  

    Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

    DOI： 10.1038/s41467-024-47989-9

    Web of Science

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    PubMed

  • Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale(タイトル和訳中)

    Takabayashi Kaoru, Kobayashi Taku, Matsuoka Katsuyoshi, Levesque Barrett G., Kawamura Takuji, Tanaka Kiyohito, Kadota Takeaki, Bise Ryoma, Uchida Seiichi, Kanai Takanori, Ogata Haruhiko

    Digestive Endoscopy   36 ( 5 )   582 - 590   2024.5   （ ISSN:0915-5635 ）

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    Language：English   Publisher：John Wiley & Sons Australia, Ltd  

  • Profiling English sentences based on CEFR levels

    Uchida, S; Arase, Y; Kajiwara, T

    ITL-INTERNATIONAL JOURNAL OF APPLIED LINGUISTICS   2024.3   （ ISSN:0019-0829 eISSN:1783-1490 ）

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  • Towards Diverse and Consistent Typography Generation Reviewed

    Wataru Shimoda, Daichi Haraguchi, Seiichi Uchida, Kota Yamaguchi

    Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2024)   2024.1

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    Language：English   Publishing type：Research paper (international conference proceedings)  

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  • Towards Diverse and Consistent Typography Generation

    Shimoda W., Haraguchi D., Uchida S., Yamaguchi K.

    Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024   7281 - 7290   2024.1   （ ISBN:9798350318920 ）

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    Publisher：Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024  

    In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document. We formulate typography generation as a fine-grained attribute generation for multiple text elements and build an autoregressive model to generate diverse typography that matches the input design context. We further propose a simple yet effective sampling approach that respects the consistency and distinction principle of typography so that generated examples share consistent typographic styling across text elements. Our empirical study shows that our model successfully generates diverse typographic designs while preserving a consistent typographic structure.

    DOI： 10.1109/WACV57701.2024.00713

    Scopus

  • Towards Diverse and Consistent Typography Generation Reviewed

    Wataru Shimoda, Daichi Haraguchi, Seiichi Uchida, Kota Yamaguchi

    Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2024)   2024.1

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    Language：English   Publishing type：Research paper (other academic)  

  • No regret sample selection with noisy labels

    Heon Song, Nariaki Mitsuo, Seiichi Uchida, Daiki Suehiro

    Machine Learning   113 ( 3 )   1163 - 1188   2024.1   （ ISSN:0885-6125 eISSN:1573-0565 ）

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    Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1007/s10994-023-06478-8

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    Other Link： https://link.springer.com/article/10.1007/s10994-023-06478-8/fulltext.html

  • What Text Design Characterizes Book Genres?

    Haraguchi D., Iwana B.K., Uchida S.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14994 LNCS   165 - 181   2024   （ ISSN:03029743 ISBN:9783031704413 ）

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    Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

    This study analyzes the relationship between non-verbal information (e.g., genres) and text design (e.g., font style, character color, etc.) through the classification of book genres using text design on book covers. Text images have both semantic information about the word itself and other information (non-semantic information or visual design), such as font style, character color, etc. When we read a word printed on some materials, we receive impressions and other information from both the word itself and the visual design. In other words, we can understand verbal information from semantic information, i.e., the words themselves; however, we can consider that text design is helpful for understanding other additional information (i.e., non-verbal information), such as impressions, genre, etc. To investigate the effect of text design, we analyze text design using words printed on book covers and their genres in two scenarios. First, we attempted to understand the importance of visual design for determining the genre (i.e., non-verbal information) of books by analyzing the differences in the relationship between semantic information/visual design and genres. In the experiment, we found that semantic information is sufficient to determine the genre; however, text design is helpful in adding more discriminative features for book genres. Second, we investigated the effect of each text design on book genres. As a result, we found that each text design characterizes some book genres. For example, font style is useful to add more discriminative features for genres of “Mystery, Thriller & Suspense” and “Christian books & Bibles”.

    DOI： 10.1007/978-3-031-70442-0_10

    Scopus

  • Typographic Text Generation with Off-the-Shelf Diffusion Model

    Peong K.T., Uchida S., Haraguchi D.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14805 LNCS   52 - 69   2024   （ ISSN:03029743 ISBN:9783031705359 ）

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    Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

    Recent diffusion-based generative models show promise in their ability to generate text images, but limitations in specifying the styles of the generated texts render them insufficient in the realm of typographic design. This paper proposes a typographic text generation system to add and modify text on typographic designs while specifying font styles, colors, and text effects. The proposed system is a novel combination of two off-the-shelf methods for diffusion models, ControlNet and Blended Latent Diffusion. The former functions to generate text images under the guidance of edge conditions specifying stroke contours. The latter blends latent noise in Latent Diffusion Models (LDM) to add typographic text naturally onto an existing background. We first show that given appropriate text edges, ControlNet can generate texts in specified fonts while incorporating effects described by prompts. We further introduce text edge manipulation as an intuitive and customizable way to produce texts with complex effects such as “shadows” and “reflections”. Finally, with the proposed system, we successfully add and modify texts on a predefined background while preserving its overall coherence.

    DOI： 10.1007/978-3-031-70536-6_4

    Scopus

  • Learning to Kern: Set-Wise Estimation of Optimal Letter Space

    Nakatsuru K., Uchida S.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14805 LNCS   18 - 34   2024   （ ISSN:03029743 ISBN:9783031705359 ）

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    Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

    Kerning is the task of setting appropriate horizontal spaces for all possible letter pairs of a certain font. One of the difficulties of kerning is that the appropriate space differs for each letter pair. Therefore, for a total of 52 capital and small letters, we need to adjust 52×52=2704 different spaces. Another difficulty is that there is neither a general procedure nor criterion for automatic kerning; therefore, kerning is still done manually or with heuristics. In this paper, we tackle kerning by proposing two machine-learning models, called pairwise and set-wise models. The former is a simple deep neural network that estimates the letter space for two given letter images. In contrast, the latter is a transformer-based model that estimates the letter spaces for three or more given letter images. For example, the set-wise model simultaneously estimates 2704 spaces for 52 letter images for a certain font. Among the two models, the set-wise model is not only more efficient but also more accurate because its internal self-attention mechanism allows for more consistent kerning for all letters. Experimental results on about 2500 Google fonts and their quantitative and qualitative analyses show that the set-wise model has an average estimation error of only about 5.3 pixels when the average letter space of all fonts and letter pairs is about 115 pixels.

    DOI： 10.1007/978-3-031-70536-6_2

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  • Impression-CLIP: Contrastive Shape-Impression Embedding for Fonts

    Kubota Y., Haraguchi D., Uchida S.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14805 LNCS   70 - 85   2024   （ ISSN:03029743 ISBN:9783031705359 ）

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    Fonts convey different impressions to readers. These impressions often come from the font shapes. However, the correlation between fonts and their impression is weak and unstable because impressions are subjective. To capture such weak and unstable cross-modal correlation between font shapes and their impressions, we propose Impression-CLIP, which is a novel machine-learning model based on CLIP (Contrastive Language-Image Pre-training). By using the CLIP-based model, font image features and their impression features are pulled closer, and font image features and unrelated impression features are pushed apart. This procedure realizes co-embedding between font image and their impressions. In our experiment, we perform cross-modal retrieval between fonts and impressions through co-embedding. The results indicate that Impression-CLIP achieves better retrieval accuracy than the state-of-the-art method. Additionally, our model shows the robustness to noise and missing tags.

    DOI： 10.1007/978-3-031-70536-6_5

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  • Font Style Interpolation with Diffusion Models

    Kondo T., Takezaki S., Haraguchi D., Uchida S.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14805 LNCS   86 - 103   2024   （ ISSN:03029743 ISBN:9783031705359 ）

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    Fonts have huge variations in their styles and give readers different impressions. Therefore, generating new fonts is worthy of giving new impressions to readers. In this paper, we employ diffusion models to generate new font styles by interpolating a pair of reference fonts with different styles. More specifically, we propose three different interpolation approaches, image-blending, condition-blending, and noise-blending, with the diffusion models. We perform qualitative and quantitative experimental analyses to understand the style generation ability of the three approaches. According to experimental results, three proposed approaches can generate not only expected font styles but also somewhat serendipitous font styles. We also compare the approaches with a state-of-the-art style-conditional Latin-font generative network model to confirm the validity of using the diffusion models for the style interpolation task.

    DOI： 10.1007/978-3-031-70536-6_6

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  • Font Impression Estimation in the Wild

    Kitajima K., Haraguchi D., Uchida S.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14805 LNCS   35 - 51   2024   （ ISSN:03029743 ISBN:9783031705359 ）

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    This paper addresses the challenging task of estimating font impressions from real font images. We use a font dataset with annotation about font impressions and a convolutional neural network (CNN) framework for this task. However, impressions attached to individual fonts are often missing and noisy because of the subjective characteristic of font impression annotation. To realize stable impression estimation even with such a dataset, we propose an exemplar-based impression estimation approach, which relies on a strategy of ensembling impressions of exemplar fonts that are similar to the input image. In addition, we train CNN with synthetic font images that mimic scanned word images so that CNN estimates impressions of font images in the wild. We evaluate the basic performance of the proposed estimation method quantitatively and qualitatively. Then, we conduct a correlation analysis between book genres and font impressions on real book cover images; it is important to note that this analysis is only possible with our impression estimation method. The analysis reveals various trends in the correlation between them—this fact supports a hypothesis that book cover designers carefully choose a font for a book cover considering the impression given by the font.

    DOI： 10.1007/978-3-031-70536-6_3

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  • Cross-Domain Image Conversion by CycleDM

    Shimotsumagari S., Takezaki S., Haraguchi D., Uchida S.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   14807 LNCS   389 - 406   2024   （ ISSN:03029743 ISBN:9783031705458 ）

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    The purpose of this paper is to enable the conversion between machine-printed character images (i.e., font images) and handwritten character images through machine learning. For this purpose, we propose a novel unpaired image-to-image domain conversion method, CycleDM, which incorporates the concept of CycleGAN into the diffusion model. Specifically, CycleDM has two internal conversion models that bridge the denoising processes of two image domains. These conversion models are efficiently trained without explicit correspondence between the domains. By applying machine-printed and handwritten character images to the two modalities, CycleDM realizes the conversion between them. Our experiments for evaluating the converted images quantitatively and qualitatively found that ours performs better than other comparable approaches.

    DOI： 10.1007/978-3-031-70546-5_23

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  • An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels

    Takezaki S., Uchida S.

    Proceedings - International Symposium on Biomedical Imaging   2024   （ ISSN:19457928 ISBN:9798350313338 ）

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    Diffusion models have recently been used for medical image generation because of their high image quality. In this study, we focus on generating medical images with ordinal classes, which have ordinal relationships, such as severity levels. We propose an Ordinal Diffusion Model (ODM) that controls the ordinal relationships of the estimated noise images among the classes. Our model was evaluated experimentally by generating retinal and endoscopic images of multiple severity classes. ODM achieved higher performance than conventional generative models by generating realistic images, especially in high-severity classes with fewer training samples.

    DOI： 10.1109/ISBI56570.2024.10635504

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  • Paired contrastive feature for highly reliable offline signature verification Reviewed

    Xiaotong ji, Daiki Suehiro, Seiichi Uchida

    Pattern Recognition   144   109816 - 109816   2023.12   （ ISSN:0031-3203 eISSN:1873-5142 ）

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    DOI： 10.1016/j.patcog.2023.109816

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  • Toward Defensive Letter Design.

    Rentaro Kataoka, Akisato Kimura, Seiichi Uchida

    Proceedings of Asian Conference on Pattern Recognition (ACPR)   14406 LNCS   108 - 122   2023.11   （ ISSN:03029743 ISBN:9783031476334 ）

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    DOI： 10.1007/978-3-031-47634-1_9

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  • Selective Scene Text Removal.

    Hayato Mitani, Akisato Kimura, Seiichi Uchida

    Britich Machine Vision Conference (BMVC)   521 - 521   2023.11

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  • Selective Scene Text Removal.

    Hayato Mitani, Akisato Kimura, Seiichi Uchida

    Britich Machine Vision Conference (BMVC)   521 - 521   2023.11

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  • Functional Knowledge Transfer with Self-supervised Representation Learning Reviewed

    Prakash Chandra Chhipa, Muskan Chopra, Gopal Mengi, Varun Gupta, Richa Upadhyay, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki

    Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP2023)   2023.10

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  • Functional Knowledge Transfer with Self-supervised Representation Learning Reviewed

    Prakash Chandra Chhipa, Muskan Chopra, Gopal Mengi, Varun Gupta, Richa Upadhyay, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki

    Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP2023)   2023.10

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  • Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. International journal

    Kaoru Takabayashi, Taku Kobayashi, Katsuyoshi Matsuoka, Barrett G Levesque, Takuji Kawamura, Kiyohito Tanaka, Takeaki Kadota, Ryoma Bise, Seiichi Uchida, Takanori Kanai, Haruhiko Ogata

    Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society   36 ( 5 )   582 - 590   2023.9   （ ISSN:0915-5635 eISSN:1443-1661 ）

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    OBJECTIVES: Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. However, inflammatory bowel disease (IBD) expert endoscopists categorize the severity and diagnose the overall impression of the degree of inflammation. This study aimed to develop an artificial intelligence (AI) system that can accurately represent the assessment of the endoscopic severity of UC by IBD expert endoscopists. METHODS: A ranking-convolutional neural network (ranking-CNN) was trained using comparative information on the UC severity of 13,826 pairs of endoscopic images created by IBD expert endoscopists. Using the trained ranking-CNN, the UC Endoscopic Gradation Scale (UCEGS) was used to express severity. Correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using UCEGS diagnosed by the AI and the Mayo Endoscopic Subscore, and the correlation coefficients of the mean for test images assessed using UCEGS by four IBD expert endoscopists and the AI. RESULTS: Spearman's correlation coefficient between the UCEGS diagnosed by AI and Mayo Endoscopic Subscore was approximately 0.89. The correlation coefficients between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (P < 0.01). CONCLUSIONS: The AI developed here can diagnose UC severity endoscopically similar to IBD expert endoscopists.

    DOI： 10.1111/den.14677

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  • FETNet: Feature Erasing and Transferring Network for Scene Text Removal (vol 140, 109531, 2023)

    Lyu, G; Liu, K; Zhu, AN; Uchida, S; Iwana, BK

    PATTERN RECOGNITION   141   2023.9   （ ISSN:0031-3203 eISSN:1873-5142 ）

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    The authors regret to inform that: The FETNet results in Table 3 and Full model results in Table 2 on the SCUT-EnsText dataset should be 34.53(PSNR), 97.01(MSSIM), 0.0013(MSE), 1.7539(AGE), 0.0137(pEPs), 0.0080(pCEPs). The authors would like to apologize for any inconvenience caused.

    DOI： 10.1016/j.patcog.2023.109581

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  • FETNet: Feature erasing and transferring network for scene text removal. Reviewed

    Guangtao Lyu, Kun Liu, Anna Zhu, Seiichi Uchida, Brian Kenji Iwana

    Pattern Recognit.   140   109531 - 109531   2023.8   （ ISSN:0031-3203 eISSN:1873-5142 ）

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  • Local Style Awareness of Font Images. Reviewed

    Daichi Haraguchi, Seiichi Uchida

    ICDAR Workshops (2)   14194 LNCS   242 - 256   2023.8   （ ISSN:03029743 ISBN:9783031415005 ）

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    DOI： 10.1007/978-3-031-41501-2_17

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  • Contour Completion by Transformers and Its Application to Vector Font Data. Reviewed

    Yusuke Nagata, Brian Kenji Iwana, Seiichi Uchida

    ICDAR (5)   14191 LNCS   490 - 504   2023.8   （ ISSN:03029743 ISBN:9783031417337 ）

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    DOI： 10.1007/978-3-031-41734-4_30

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  • Analyzing Font Style Usage and Contextual Factors in Real Images. Reviewed

    Naoya Yasukochi, Hideaki Hayashi, Daichi Haraguchi, Seiichi Uchida

    ICDAR (3)   14189 LNCS   331 - 347   2023.8   （ ISSN:03029743 ISBN:9783031416811 ）

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    DOI： 10.1007/978-3-031-41682-8_21

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  • Ambigram Generation by a Diffusion Model. Reviewed

    Takahiro Shirakawa, Seiichi Uchida

    ICDAR (3)   314 - 330   2023.8

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    DOI： 10.1007/978-3-031-41682-8_20

  • Application of deep learning diagnosis for multiple traits sorting in peach fruit

    Kanae Masuda, Rika Uchida, Naoko Fujita, Yoshiaki Miyamoto, Takahiro Yasue, Yasutaka Kubo, Koichiro Ushijima, Seiichi Uchida, Takashi Akagi

    Postharvest Biology and Technology   201   112348 - 112348   2023.7   （ ISSN:0925-5214 eISSN:1873-2356 ）

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    DOI： 10.1016/j.postharvbio.2023.112348

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  • Learning From Label Proportion with Online Pseudo-Label Decision by Regret Minimization Reviewed

    Shinnosuke Matsuo, Ryoma Bise, Seiichi Uchida, Daiki Suehiro

    ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   2023.6   （ ISSN:15206149 ）

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    DOI： 10.1109/icassp49357.2023.10097069

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  • Transcriptomic interpretation on explainable AI-guided intuition uncovers premonitory reactions of disordering fate in persimmon fruit. Reviewed

    Kanae Masuda, Eriko Kuwada, Maria Suzuki, Tetsuya Suzuki, Takeshi Niikawa, Seiichi Uchida, Takashi Akagi

    Plant & cell physiology   64 ( 11 )   1323 - 1330   2023.5   （ ISSN:0032-0781 eISSN:1471-9053 ）

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    Deep neural network (DNN) techniques, as an advanced machine learning framework, have allowed various image diagnoses in plants, which often achieve better prediction performance than human experts in each specific field. Notwithstanding, in plant biology, the application of deep neural networks is still mostly limited to rapid and effective phenotyping. Recent development of explainable CNN frameworks has allowed visualization of the features in the prediction by convolutional neural network (CNN), which potentially contributes to the understanding of physiological mechanisms in objective phenotypes. In this study, we propose an integration of explainable CNN and transcriptomic approach to make a physiological interpretation of a fruit internal disorder in persimmon, rapid over-softening. We constructed CNN models to accurately predict the fate to be rapid softening in persimmon cv. Soshu, only with photo images. The explainable CNNs, such as Grad-CAM and Guided Grad-CAM, visualized specific featured regions relevant to the prediction of rapid-softening, which would correspond to the premonitory symptoms in a fruit. Transcriptomic analyses to compare the featured regions of predicted rapid-softening and control fruits suggested that rapid softening is triggered by precocious ethylene signal-dependent cell wall modification, despite exhibiting no direct phenotypic changes. Further transcriptomic comparison between the featured and non-featured regions in predicted rapid-softening fruit suggested that premonitory symptoms reflected hypoxia and the related stress signals finally to induce ethylene signals. These results would provide a good example for the collaboration of image analysis and omics approaches in plant physiology, which uncovered a novel aspect of fruit premonitory reactions in the rapid softening fate.

    DOI： 10.1093/pcp/pcad050

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  • Disease Severity Regression with Continuous Data Augmentation Reviewed

    Shumpei Takezaki, Kiyohito Tanaka, Seiichi Uchida, Takeaki Kadota

    Proceedings of IEEE International Symposium on Biomedical Imaging   2023-April   1 - 5   2023.4   （ ISSN:1945-7928 ISBN:978-1-6654-7358-3 ）

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    Disease Severity Regression with Continuous Data Augmentation

    DOI： 10.1109/ISBI53787.2023.10230453

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  • Deep attentive time warping. Reviewed

    Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan, Akisato Kimura, Kunio Kashino, Brian Kenji Iwana, Seiichi Uchida

    Pattern Recognit.   136   109201 - 109201   2023.4   （ ISSN:0031-3203 eISSN:1873-5142 ）

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  • Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification Reviewed

    Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida

    Proceedings of IEEE International;Symposium on Biomedical Imaging   2023-April   1 - 5   2023.4   （ ISSN:1945-7928 ISBN:978-1-6654-7358-3 ）

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    Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

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  • Depth Contrast: Self-supervised Pretraining on 3DPM Images for Mining Material Classification Reviewed

    Prakash Chandra Chhipa, Richa Upadhyay, Rajkumar Saini, Lars Lindqvist, Richard Nordenskjold, Seiichi Uchida, Marcus Liwicki

    Lecture Notes in Computer Science   13807 LNCS   212 - 227   2023.2   （ ISSN:0302-9743 ISBN:9783031250811, 9783031250828 eISSN:1611-3349 ）

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    DOI： 10.1007/978-3-031-25082-8_14

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  • Development of a simultaneous electrorotation device with microwells for monitoring the rotation rates of multiple single cells upon chemical stimulation. Reviewed International journal

    Masato Suzuki, Shikiho Kawai, Chean Fei Shee, Ryoga Yamada, Seiichi Uchida, Tomoyuki Yasukawa

    Lab on a chip   23 ( 4 )   692 - 701   2023.2   （ ISSN:1473-0197 eISSN:1473-0189 ）

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    Here, we described a unique simultaneous electrorotation (ROT) device for monitoring the rotation rate of Jurkat cells via chemical stimulation without fluorescent labeling and an algorithm for estimating cell rotation rates. The device comprised two pairs of interdigitated array electrodes that were stacked orthogonally through a 20 μm-thick insulating layer with rectangular microwells. Four microelectrodes (two were patterned on the bottom of the microwells and the other two on the insulating layer) were arranged on each side of the rectangular microwells. The cells, which were trapped in the microwells, underwent ROT when AC voltages were applied to the four microelectrodes to generate a rotating electric field. These microwells maintained the cells even in fluid flows. Thereafter, the ROT rates of the trapped cells were estimated and monitored during the stimulation. We demonstrated the feasibility of estimating the chemical efficiency of cells by monitoring the ROT rates of the cells. After introducing a Jurkat cell suspension into the device, the cells were subjected to ROT by applying an AC signal. Further, the rotating cells were chemically stimulated by adding an ionomycin (a calcium ionophore)-containing aliquot. The ROT rate of the ionomycin-stimulated cells decreased gradually to 90% of the initial rate after 30 s. The ROT rate was reduced by an increase in membrane capacitance. Thus, our device enabled the simultaneous chemical stimulation-induced monitoring of the alterations in the membrane capacitances of many cells without fluorescent labeling.

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  • Development of a chub mackerel with less-aggressive fry stage by genome editing of arginine vasotocin receptor V1a2. Reviewed International journal

    Hirofumi Ohga, Koki Shibata, Ryo Sakanoue, Takuma Ogawa, Hajime Kitano, Satoshi Kai, Kohei Ohta, Naoki Nagano, Tomoya Nagasako, Seiichi Uchida, Tetsushi Sakuma, Takashi Yamamoto, Sangwan Kim, Kosuke Tashiro, Satoru Kuhara, Koichiro Gen, Atushi Fujiwara, Yukinori Kazeto, Takanori Kobayashi, Michiya Matsuyama

    Scientific reports   13 ( 1 )   3190 - 3190   2023.2   （ ISSN:2045-2322 ）

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    Genome editing is a technology that can remarkably accelerate crop and animal breeding via artificial induction of desired traits with high accuracy. This study aimed to develop a chub mackerel variety with reduced aggression using an experimental system that enables efficient egg collection and genome editing. Sexual maturation and control of spawning season and time were technologically facilitated by controlling the photoperiod and water temperature of the rearing tank. In addition, appropriate low-temperature treatment conditions for delaying cleavage, shape of the glass capillary, and injection site were examined in detail in order to develop an efficient and robust microinjection system for the study. An arginine vasotocin receptor V1a2 (V1a2) knockout (KO) strain of chub mackerel was developed in order to reduce the frequency of cannibalistic behavior at the fry stage. Video data analysis using bioimage informatics quantified the frequency of aggressive behavior, indicating a significant 46% reduction (P = 0.0229) in the frequency of cannibalistic behavior than in wild type. Furthermore, in the V1a2 KO strain, the frequency of collisions with the wall and oxygen consumption also decreased. Overall, the manageable and calm phenotype reported here can potentially contribute to the development of a stable and sustainable marine product.

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  • Artificial Intelligence Quantifying Endoscopic Severity of Ulcerative Colitis in Gradation Scale

    Takabayashi, K; Kobayashi, T; Matsuoka, K; Levesque, BG; Kawamura, T; Tanaka, K; Kadota, T; Bise, R; Uchida, S; Kanai, T; Ogata, H

    JOURNAL OF CROHNS & COLITIS   17   I151 - I152   2023.2   （ ISSN:1873-9946 eISSN:1876-4479 ）

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  • Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images Reviewed

    Prakash Chandra Chhipa, Richa Upadhyay, Gustav Grund Pihlgren, Rajkumar Saini, Seiichi Uchida, Marcus Liwicki

    2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)   2716 - 2726   2023.1   （ ISSN:2472-6737 ISBN:978-1-6654-9346-8 ）

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    DOI： 10.1109/wacv56688.2023.00274

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  • Interferon signaling and hypercytokinemia-related gene expression in the blood of antidepressant non-responders

    Yamagata, H; Tsunedomi, R; Kamishikiryo, T; Kobayashi, A; Seki, T; Kobayashi, M; Hagiwara, K; Yamada, N; Chen, C; Uchida, S; Ogihara, H; Hamamoto, Y; Okada, G; Fuchikami, M; Iga, J; Numata, S; Kinoshita, M; Kato, TA; Hashimoto, R; Nagano, H; Ueno, S; Okamoto, Y; Ohmori, T; Nakagawa, S

    HELIYON   9 ( 1 )   e13059   2023.1   （ ISSN:2405-8440 eISSN:2405-8440 ）

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  • Functional Knowledge Transfer with Self-supervised Representation Learning

    Chhipa, PC; Chopra, M; Mengi, G; Gupta, V; Upadhyay, R; Chippa, MS; De, K; Saini, R; Uchida, S; Liwicki, M

    2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP   3339 - 3343   2023   （ ISSN:1522-4880 ISBN:978-1-7281-9835-4 ）

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    This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization of self-supervised learning pseudo task and supervised learning task, improving supervised learning task performance. Recent progress in self-supervised learning uses a large volume of data, which becomes a constraint for its applications on small-scale datasets. This work shares a simple yet effective joint training framework that reinforces human-supervised task learning by learning self-supervised representations just-in-time and vice versa. Experiments on three public datasets from different visual domains, Intel Image, CIFAR, and APTOS, reveal a consistent track of performance improvements on classification tasks during joint optimization. Qualitative analysis also supports the robustness of learnt representations. Source code and trained models are available on GitHub1

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  • Distractor Generation for Fill-in-the-Blank Exercises by Question Type

    Yoshimi, N; Kajiwara, T; Uchida, S; Arase, Y; Ninomiya, T

    PROCEEDINGS OF THE 61ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL-SRW 2023, VOL 4   276 - 281   2023   （ ISBN:978-1-959429-69-2 ）

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  • Boosting for Bounding the Worst-class Error.

    Yuya Saito, Shinnosuke Matsuo, Seiichi Uchida, Daiki Suehiro

    CoRR   abs/2310.14890   2023

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  • Deep learning-based automatic-bone-destruction-evaluation system using contextual information from other joints Reviewed

    Kazuki Miyama, Ryoma Bise, Satoshi Ikemura, Kazuhiro Kai, Masaya Kanahori, Shinkichi Arisumi, Taisuke Uchida, Yasuharu Nakashima, Seiichi Uchida

    Arthritis Research and Therapy   24 ( 1 )   227   2022.12   （ ISSN:1478-6354 eISSN:1478-6362 ）

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    Background: X-ray images are commonly used to assess the bone destruction of rheumatoid arthritis. The purpose of this study is to propose an automatic-bone-destruction-evaluation system fully utilizing deep neural networks (DNN). This system detects all target joints of the modified Sharp/van der Heijde score (SHS) from a hand X-ray image. It then classifies every target joint as intact (SHS = 0) or non-intact (SHS ≥ 1). Methods: We used 226 hand X-ray images of 40 rheumatoid arthritis patients. As for detection, we used a DNN model called DeepLabCut. As for classification, we built four classification models that classify the detected joint as intact or non-intact. The first model classifies each joint independently, whereas the second model does it while comparing the same contralateral joint. The third model compares the same joint group (e.g., the proximal interphalangeal joints) of one hand and the fourth model compares the same joint group of both hands. We evaluated DeepLabCut’s detection performance and classification models’ performances. The classification models’ performances were compared to three orthopedic surgeons. Results: Detection rates for all the target joints were 98.0% and 97.3% for erosion and joint space narrowing (JSN). Among the four classification models, the model that compares the same contralateral joint showed the best F-measure (0.70, 0.81) and area under the curve of the precision-recall curve (PR-AUC) (0.73, 0.85) regarding erosion and JSN. As for erosion, the F-measure and PR-AUC of this model were better than the best of the orthopedic surgeons. Conclusions: The proposed system was useful. All the target joints were detected with high accuracy. The classification model that compared the same contralateral joint showed better performance than the orthopedic surgeons regarding erosion.

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  • Font Generation with Missing Impression Labels Reviewed

    Seiya Matsuda, Akisato Kimura, Seiichi Uchida

    2022 26th International Conference on Pattern Recognition (ICPR)   2022-August   1400 - 1406   2022.8   （ ISSN:1051-4651 ISBN:978-1-6654-9062-7 ）

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    DOI： 10.1109/icpr56361.2022.9956147

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  • MontageGAN: Generation and Assembly of Multiple Components by GANs Reviewed

    Chean Fei Shee, Seiichi Uchida

    2022 26th International Conference on Pattern Recognition (ICPR)   2022-August   1478 - 1484   2022.8   （ ISSN:1051-4651 ISBN:978-1-6654-9062-7 ）

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    DOI： 10.1109/icpr56361.2022.9956028

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  • Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data. Reviewed

    Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida

    Medical Image Understanding and Analysis: 26th Annual Conference, MIUA 2022   13413   609 - 622   2022.7   （ ISSN:0302-9743 ISBN:978-3-031-12052-7 eISSN:1611-3349 ）

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    DOI： 10.1007/978-3-031-12053-4_45

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  • Fonts That Fit the Music: A Multimodal Design Trend Analysis of Lyric Videos Reviewed

    Daichi Haraguchi, Shota Sakaguchi, Jun Kato, Masataka Goto, Seiichi Uchida

    IEEE Access   10   65414 - 65425   2022.6   （ ISSN:2169-3536 eISSN:2169-3536 ）

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    DOI： 10.1109/access.2022.3184028

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  • Shared Latent Space of Font Shapes and Their Noisy Impressions Reviewed

    Jihun Kang, Daichi Haraguchi, Seiya Matsuda, Akisato Kimura, Seiichi Uchida

    MultiMedia Modeling   13142   146 - 157   2022.6   （ ISSN:0302-9743 ISBN:978-3-030-98354-3 eISSN:1611-3349 ）

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    DOI： 10.1007/978-3-030-98355-0_13

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  • Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning. Reviewed International journal

    Takashi Akagi, Kanae Masuda, Eriko Kuwada, Kouki Takeshita, Taiji Kawakatsu, Tohru Ariizumi, Yasutaka Kubo, Koichiro Ushijima, Seiichi Uchida

    The Plant cell   34 ( 6 )   2174 - 2187   2022.5   （ ISSN:1040-4651 eISSN:1532-298X ）

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    In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression.

    DOI： 10.1093/plcell/koac079

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  • Deep Learning Predicts Rapid Over-softening and Shelf Life in Persimmon Fruits Reviewed

    Maria Suzuki, Kanae Masuda, Hideaki Asakuma, Kouki Takeshita, Kohei Baba, Yasutaka Kubo, Koichiro Ushijima, Seiichi Uchida, Takashi Akagi

    The Horticulture Journal   91 ( 3 )   408 - 415   2022.5   （ ISSN:21890102 eISSN:21890110 ）

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    <p>In contrast to the progress in the research on physiological disorders relating to shelf life in fruit crops, it has been difficult to non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various disorders in some crops, while there are still issues in terms of efficiency and costs. Here, we propose application of a deep neural network (or simply deep learning) to simple RGB images to predict a severe fruit disorder in persimmon, rapid over-softening. With 1,080 RGB images of ‘Soshu’ persimmon fruits, three convolutional neural networks (CNN) were examined to predict rapid over-softened fruits with a binary classification and the date to fruit softening. All of the examined CNN models worked successfully for binary classification of the rapid over-softened fruits and the controls with > 80% accuracy using multiple criteria. Furthermore, the prediction values (or confidence) in the binary classification were correlated to the date to fruit softening. Although the features for classification by deep learning have been thought to be in a black box by conventional standards, recent feature visualization methods (or “explainable” deep learning) has allowed identification of the relevant regions in the original images. We applied Grad-CAM, Guided backpropagation, and layer-wise relevance propagation (LRP), to find early symptoms for CNNs classification of rapid over-softened fruits. The focus on the relevant regions tended to be on color unevenness on the surface of the fruit, especially in the peripheral regions. These results suggest that deep learning frameworks could potentially provide new insights into early physiological symptoms of which researchers are unaware.</p>

    DOI： 10.2503/hortj.utd-323

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  • TrueType Transformer: Character and Font Style Recognition in Outline Format

    Yusuke Nagata, Jinki Otao, Daichi Haraguchi, Seiichi Uchida

    Document Analysis Systems   13237   18 - 32   2022.5   （ ISSN:0302-9743 ISBN:978-3-031-06554-5 eISSN:1611-3349 ）

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    DOI： 10.1007/978-3-031-06555-2_2

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  • Revealing Reliable Signatures by Learning Top-Rank Pairs

    Xiaotong Ji, Yan Zheng, Daiki Suehiro, Seiichi Uchida

    Document Analysis Systems   13237   323 - 337   2022.5   （ ISSN:0302-9743 ISBN:978-3-031-06554-5 eISSN:1611-3349 ）

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    DOI： 10.1007/978-3-031-06555-2_22

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  • Font Shape-to-Impression Translation

    Masaya Ueda, Akisato Kimura, Seiichi Uchida

    Document Analysis Systems   13237   3 - 17   2022.5   （ ISSN:0302-9743 ISBN:978-3-031-06554-5 eISSN:1611-3349 ）

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    DOI： 10.1007/978-3-031-06555-2_1

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  • 文字とは何か? : 深層学習により見えてきた新たな問い—What Are Letters? : A New Horizon of Document Image Analysis Research by Deep Learning—特集 深層学習は情報・システムの研究をどう変えたか ; 画像分野

    内田 誠一

    電子情報通信学会誌 = The journal of the Institute of Electronics, Information and Communication Engineers   105 ( 5 )   371 - 374   2022.5

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  • Tricellulin secures the epithelial barrier at tricellular junctions by interacting with actomyosin. International journal

    Yuma Cho, Daichi Haraguchi, Kenta Shigetomi, Kenji Matsuzawa, Seiichi Uchida, Junichi Ikenouchi

    The Journal of cell biology   221 ( 4 )   2022.4   （ ISSN:0021-9525 eISSN:1540-8140 ）

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    The epithelial cell sheet functions as a barrier to prevent invasion of pathogens. It is necessary to eliminate intercellular gaps not only at bicellular junctions, but also at tricellular contacts, where three cells meet, to maintain epithelial barrier function. To that end, tight junctions between adjacent cells must associate as closely as possible, particularly at tricellular contacts. Tricellulin is an integral component of tricellular tight junctions (tTJs), but the molecular mechanism of its contribution to the epithelial barrier function remains unclear. In this study, we revealed that tricellulin contributes to barrier formation by regulating actomyosin organization at tricellular junctions. Furthermore, we identified α-catenin, which is thought to function only at adherens junctions, as a novel binding partner of tricellulin. α-catenin bridges tricellulin attachment to the bicellular actin cables that are anchored end-on at tricellular junctions. Thus, tricellulin mobilizes actomyosin contractility to close the lateral gap between the TJ strands of the three proximate cells that converge on tricellular junctions.

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  • Automatic Estimation of Ulcerative Colitis Severity by Learning to Rank With Calibration.

    Takeaki Kadota, Kentaro Abe, Ryoma Bise, Takuji Kawamura, Naokuni Sakiyama, Kiyohito Tanaka, Seiichi Uchida

    IEEE Access   10   25688 - 25695   2022.3   （ ISSN:2169-3536 ）

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  • 文字とは何か？ ― 深層学習により見えてきた新たな問い―

    内田 誠一

    電子情報通信学会誌   105   371 - 374   2022

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    608 - 619   2021.12

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    DOI： 10.1007/978-3-030-92238-2_50

  • Top-rank convolutional neural network and its application to medical image-based diagnosis.

    Yan Zheng, Yuchen Zheng, Daiki Suehiro, Seiichi Uchida

    Pattern Recognition   120   108138 - 108138   2021.12

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    DOI： 10.1016/j.patcog.2021.108138

  • Discovery of anti-inflammatory physiological peptides that promote tissue repair by reinforcing epithelial barrier formation. International journal

    Yukako Oda, Chisato Takahashi, Shota Harada, Shun Nakamura, Daxiao Sun, Kazumi Kiso, Yuko Urata, Hitoshi Miyachi, Yoshinori Fujiyoshi, Alf Honigmann, Seiichi Uchida, Yasushi Ishihama, Fumiko Toyoshima

    Science advances   7 ( 47 )   eabj6895   2021.11

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    DOI： 10.1126/sciadv.abj6895

  • De-rendering Stylized Texts

    Wataru Shimoda, Daichi Haraguchi, Seiichi Uchida, Kota Yamaguchi

    2021 IEEE/CVF International Conference on Computer Vision (ICCV)   2021.10

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    DOI： 10.1109/iccv48922.2021.00111

  • Learning the micro deformations by max-pooling for offline signature verification Reviewed

    Yuchen Zheng, Brian Kenji Iwana, Muhammad Imran Malik, Sheraz Ahmed, Wataru Ohyama, Seiichi Uchida

    Pattern Recognition   118   108008 - 108008   2021.10

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    DOI： 10.1016/j.patcog.2021.108008

  • Which Parts Determine the Impression of the Font?

    Masaya Ueda, Akisato Kimura, Seiichi Uchida

    16th International Conference on Document Analysis and Recognition   12823 LNCS   723 - 738   2021.9

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    DOI： 10.1007/978-3-030-86334-0_47

  • Using Robust Regression to Find Font Usage Trends.

    Kaigen Tsuji, Seiichi Uchida, Brian Kenji Iwana

    Document Analysis and Recognition   12917 LNCS   126 - 141   2021.9

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    DOI： 10.1007/978-3-030-86159-9_9

  • Towards Book Cover Design via Layout Graphs.

    Wensheng Zhang, Yan Zheng, Taiga Miyazono, Seiichi Uchida, Brian Kenji Iwana

    16th International Conference on Document Analysis and Recognition   12823 LNCS   642 - 657   2021.9

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    DOI： 10.1007/978-3-030-86334-0_42

  • Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels.

    Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida

    Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference   12902 LNCS   471 - 480   2021.9

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    DOI： 10.1007/978-3-030-87196-3_44

  • Meta-learning of Pooling Layers for Character Recognition.

    Takato Otsuzuki, Heon Song, Seiichi Uchida, Hideaki Hayashi

    16th International Conference on Document Analysis and Recognition   12823 LNCS   188 - 203   2021.9

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    DOI： 10.1007/978-3-030-86334-0_13

  • Impressions2Font: Generating Fonts by Specifying Impressions.

    Seiya Matsuda, Akisato Kimura, Seiichi Uchida

    16th International Conference on Document Analysis and Recognition   12823 LNCS   739 - 754   2021.9

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    DOI： 10.1007/978-3-030-86334-0_48

  • Font Style that Fits an Image - Font Generation Based on Image Context.

    Taiga Miyazono, Brian Kenji Iwana, Daichi Haraguchi, Seiichi Uchida

    16th International Conference on Document Analysis and Recognition   12823 LNCS   569 - 584   2021.9

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    DOI： 10.1007/978-3-030-86334-0_37

  • Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logo.

    Shintaro Nishi, Takeaki Kadota, Seiichi Uchida

    Document Analysis and Recognition   12916 LNCS   97 - 111   2021.9

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    DOI： 10.1007/978-3-030-86198-8_8

  • Attention to Warp: Deep Metric Learning for Multivariate Time Series.

    Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan, Akisato Kimura, Kunio Kashino, Brian Kenji Iwana, Seiichi Uchida

    16th International Conference on Document Analysis and Recognition   12823 LNCS   350 - 365   2021.9

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    DOI： 10.1007/978-3-030-86334-0_23

  • Soft and self constrained clustering for group-based labeling.

    Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida

    Medical Image Anal.   72   102097 - 102097   2021.8

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    DOI： 10.1016/j.media.2021.102097

  • An empirical survey of data augmentation for time series classification with neural networks. International journal

    Brian Kenji Iwana, Seiichi Uchida

    PloS one   16 ( 7 )   e0254841   2021.7

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    DOI： 10.1371/journal.pone.0254841

  • Tunable U-Net: Controlling Image-to-Image Outputs Using a Tunable Scalar Value.

    Seokjun Kang, Seiichi Uchida, Brian Kenji Iwana

    IEEE Access   9   103279 - 103290   2021.7

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    DOI： 10.1109/ACCESS.2021.3096530

  • Mining Neighbor Frames for Person Re-identification by Global Optimal Tracking.

    Kai Han 0002, Jinho Lee, Lang Huang, Fangcheng Liu, Seiichi Uchida, Chao Zhang 0001

    Advances in Swarm Intelligence - 12th International Conference   12690 LNCS   391 - 406   2021.7

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    DOI： 10.1007/978-3-030-78811-7_37

  • Layer-Wise Interpretation of Deep Neural Networks using Identity Initialization Reviewed

    Shohei Kubota, Hideaki Hayashi, Tomohiro Hayase, Seiichi Uchida

    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   2021-June   3945 - 3949   2021.6

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    DOI： 10.1109/icassp39728.2021.9414873

  • Self-Augmented Multi-Modal Feature Embedding Reviewed

    Shinnosuke Matsuo, Seiichi Uchida, Brian Kenji Iwana

    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)   2021-June   3995 - 3999   2021.6

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    DOI： 10.1109/icassp39728.2021.9413974

  • A Discriminative Gaussian Mixture Model with Sparsity.

    Hideaki Hayashi, Seiichi Uchida

    9th International Conference on Learning Representations(ICLR)   2021.5

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  • AIとは何か? : 入門編 (特集 さあ、AIを始めよう : 土木工学へのAI導入のススメ)—An intuitive introduction of AI

    内田 誠一

    土木学会誌   106 ( 1 )   12 - 15   2021.1

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  • Noninvasive diagnosis of seedless fruit using deep learning in persimmon

    Kanae Masuda, Maria Suzuki, Kohei Baba, Kouki Takeshita, Tetsuya Suzuki, Mayu Sugiura, Takeshi Niikawa, Seiichi Uchida, Takashi Akagi

    Horticulture Journal   90 ( 2 )   172 - 180   2021.1

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    DOI： 10.2503/hortj.UTD-248

  • Complex image processing with less data - Document image binarization by integrating multiple pre-trained U-Net modules.

    Seokjun Kang, Brian Kenji Iwana, Seiichi Uchida

    Pattern Recognit.   109   107577 - 107577   2021.1

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    DOI： 10.1016/j.patcog.2020.107577

  • Total Whitening for Online Signature Verification Based on Deep Representation

    Xiaomeng Wu, Akisato Kimura, Kunio Kashino, Seiichi Uchida

    2020 25th International Conference on Pattern Recognition (ICPR)   2021.1

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    DOI： 10.1109/icpr48806.2021.9412545

  • Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher

    Brian Kenji Iwana, Seiichi Uchida

    2020 25th International Conference on Pattern Recognition (ICPR)   2021.1

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    DOI： 10.1109/icpr48806.2021.9412812

  • STIM-Orai1 Signaling Regulates Fluidity of Cytoplasm during Membrane Blebbing Reviewed International journal

    Kana Aoki, Shota Harada, Keita Kawaji, Kenji Matsuzawa, Seiichi Uchida, and Junichi Ikenouchi

    Nature Communications   2021.1

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    DOI： 10.1038/s41467-020-20826-5

  • Label or Message: A Large-Scale Experimental Survey of Texts and Objects Co-Occurrence

    Koki Takeshita, Juntaro Shioyama, Seiichi Uchida

    2020 25th International Conference on Pattern Recognition (ICPR)   2021.1

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    DOI： 10.1109/icpr48806.2021.9412077

  • Explainable deep learning reproduces a 'Professional eye' on the diagnosis of internal disorders in persimmon fruit

    Takashi Akagi, Masanori Onishi, Kanae Masuda, Ryohei Kuroki, Kohei Baba, Kouki Takeshita, Tetsuya Suzuki, Takeshi Niikawa, Seiichi Uchida, Takeshi Ise

    Plant and Cell Physiology   61 ( 11 )   1967 - 1973   2020.11

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    DOI： 10.1093/pcp/pcaa111

  • Handwriting Prediction Considering Inter-Class Bifurcation Structures

    Masaki Yamagata, Hideaki Hayashi, Seiichi Uchida

    Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR   2020-September   103 - 108   2020.9

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    DOI： 10.1109/ICFHR2020.2020.00029

  • Regularized Pooling

    Takato Otsuzuki, Hideaki Hayashi, Yuchen Zheng, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   12397 LNCS   241 - 254   2020.9

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    DOI： 10.1007/978-3-030-61616-8_20

  • What is the Reward for Handwriting?-A Handwriting Generation Model Based on Imitation Learning

    Keisuke Kanda, Brian Kenji Iwana, Seiichi Uchida

    Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR   2020-September   109 - 114   2020.9

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    DOI： 10.1109/ICFHR2020.2020.00030

  • Lyric video analysis using text detection and tracking

    Shota Sakaguchi, Jun Kato, Masataka Goto, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   12116 LNCS   426 - 440   2020.7

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    DOI： 10.1007/978-3-030-57058-3_30

  • Effect of text color on word embeddings

    Masaya Ikoma, Brian Kenji Iwana, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   12116 LNCS   341 - 355   2020.7

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    DOI： 10.1007/978-3-030-57058-3_24

  • Character-independent font identification Reviewed

    Daichi Haraguchi, Shota Harada, Brian Kenji Iwana, Yuto Shinahara, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   12116 LNCS   497 - 511   2020.7

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    DOI： 10.1007/978-3-030-57058-3_35

  • ACMU-nets: Attention cascading modular U-nets incorporating squeeze and excitation blocks

    Seokjun Kang, Brian Kenji Iwana, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   12116 LNCS   118 - 130   2020.7

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    DOI： 10.1007/978-3-030-57058-3_9

  • Guided neural style transfer for shape stylization

    Gantugs Atarsaikhan, Brian Kenji Iwana, Seiichi Uchida

    PLoS ONE   15 ( 6 )   2020.6

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    DOI： 10.1371/journal.pone.0233489

  • Neural style difference transfer and its application to font generation Reviewed

    Gantugs Atarsaikhan, Brian Kenji Iwana, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   12116 LNCS   544 - 558   2020.6

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    DOI： 10.1007/978-3-030-57058-3_38

  • Iconify: Converting Photographs into Icons

    Takuro Karamatsu, Gibran Benitez-Garcia, Keiji Yanai, Seiichi Uchida

    MMArt-ACM 2020 - Proceedings of the 2020 Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia   7 - 12   2020.6

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    DOI： 10.1145/3379173.3393708

  • Few-Shot Text Style Transfer via Deep Feature Similarity. Reviewed

    Anna Zhu, Xiongbo Lu, Xiang Bai, Seiichi Uchida, Brian Kenji Iwana, Shengwu Xiong

    IEEE Trans. Image Process.   29   6932 - 6946   2020.5

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    DOI： 10.1109/TIP.2020.2995062

  • Benchmarking Deep Learning Models for Classification of Book Covers. Reviewed

    Adriano Lucieri, Huzaifa Sabir, Shoaib Ahmed Siddiqui, Syed Tahseen Raza Rizvi, Brian Kenji Iwana, Seiichi Uchida, Andreas Dengel 0001, Sheraz Ahmed

    SN Comput. Sci.   1 ( 3 )   139 - 139   2020.4

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    DOI： 10.1007/s42979-020-00132-z

  • Adaptive aggregation of arbitrary online trackers with a regret bound Reviewed International journal

    Heon Song, Daiki Suehiro, Seiichi Uchida

    Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020   670 - 678   2020.3

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    DOI： 10.1109/WACV45572.2020.9093613

  • Coordinated changes in cell membrane and cytoplasm during maturation of apoptotic bleb Reviewed International journal

    Kana Aoki, Shinsuke Satoi, Shota Harada, Seiichi Uchida, Yoh Iwasa, Junichi Ikenouchi

    Molecular Biology of the Cell   31 ( 8 )   833 - 844   2020.3

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    DOI： 10.1091/MBC.E19-12-0691

  • DTW-NN: A novel neural network for time series recognition using dynamic alignment between inputs and weights Reviewed

    Brian Kenji Iwana, Volkmar Frinken, Seiichi Uchida

    Knowledge-Based Systems   188   2020.1

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    DOI： 10.1016/j.knosys.2019.104971

  • Automatic Generation of Typographic Font From Small Font Subset Reviewed

    Tomo Miyazaki, Tatsunori Tsuchiya, Yoshihiro Sugaya, Shinichiro Omachi, Masakazu Iwamura, Seiichi Uchida, Koichi Kise

    IEEE Computer Graphics and Applications   40 ( 1 )   99 - 111   2020.1

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    DOI： 10.1109/mcg.2019.2931431

  • Time series classification using local distance-based features in multi-modal fusion networks Reviewed

    Brian Kenji Iwana, Seiichi Uchida

    Pattern Recognition   97   2020.1

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    DOI： 10.1016/j.patcog.2019.107024

  • GlyphGAN: Style-consistent font generation based on generative adversarial networks. Reviewed

    Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida

    Knowl.-Based Syst.   186   2019.12

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    DOI： 10.1016/j.knosys.2019.104927

  • Optimal Rejection Function Meets Character Recognition Tasks Reviewed

    Xiaotong Ji, Yuchen Zheng, Daiki Suehiro, Seiichi Uchida

    Proceedings of the 5th Asian Conference on Pattern Recognition   2019.11

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    Optimal Rejection Function Meets Character Recognition Tasks

  • Detecting Mathematical Expressions in Scientific Document Images Using a U-Net Trained on a Diverse Dataset. Reviewed

    Wataru Ohyama, Masakazu Suzuki, Seiichi Uchida

    IEEE Access   7   144030 - 144042   2019.10

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    DOI： 10.1109/ACCESS.2019.2945825

  • Explaining convolutional neural networks using softmax gradient layer-wise relevance propagation Reviewed International journal

    Brian Kenji Iwana, Ryohei Kuroki, Seiichi Uchida

    17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019   4176 - 4185   2019.10

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    DOI： 10.1109/ICCVW.2019.00513

  • Capturing micro deformations from pooling layers for offline signature verification Reviewed International journal

    Yuchen Zheng, Wataru Ohyama, Brian Kenji Iwana, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   1111 - 1116   2019.9

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    DOI： 10.1109/ICDAR.2019.00180

  • Training Convolutional Autoencoders with Metric Learning. Reviewed

    Yosuke Onitsuka, Wataru Ohyama, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   86 - 91   2019.9

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    DOI： 10.1109/ICDAR.2019.00023

  • Selective Super-Resolution for Scene Text Images. Reviewed

    Ryo Nakao, Brian Kenji Iwana, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   401 - 406   2019.9

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    DOI： 10.1109/ICDAR.2019.00071

  • Scene Text Magnifier. Reviewed

    Toshiki Nakamura, Anna Zhu, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   825 - 830   2019.9

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    DOI： 10.1109/ICDAR.2019.00137

  • RankSVM for Offline Signature Verification. Reviewed

    Yan Zheng, Yuchen Zheng, Wataru Ohyama, Daiki Suehiro, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   928 - 933   2019.9

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    DOI： 10.1109/ICDAR.2019.00153

  • On the Ability of a CNN to Realize Image-to-Image Language Conversion. Reviewed

    Kohei Baba, Seiichi Uchida, Brian Kenji Iwana

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   448 - 453   2019.9

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    DOI： 10.1109/ICDAR.2019.00078

  • Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders. Reviewed

    Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   407 - 412   2019.9

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    DOI： 10.1109/ICDAR.2019.00072

  • Mining the displacement of max-pooling for text recognition. Reviewed

    Yuchen Zheng, Brian Kenji Iwana, Seiichi Uchida

    Pattern Recognition   93   558 - 569   2019.9

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    DOI： 10.1016/j.patcog.2019.05.014

  • Deep Dynamic Time Warping: End-to-End Local Representation Learning for Online Signature Verification. Reviewed

    Xiaomeng Wu, Akisato Kimura, Brian Kenji Iwana, Seiichi Uchida, Kunio Kashino

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   1103 - 1110   2019.9

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    DOI： 10.1109/ICDAR.2019.00179

  • Cascading Modular U-Nets for Document Image Binarization. Reviewed

    Seokjun Kang, Brian Kenji Iwana, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   675 - 680   2019.9

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    DOI： 10.1109/ICDAR.2019.00113

  • Serif or sans Visual font analytics on book covers and online advertisements Reviewed International journal

    Yuto Shinahara, Takuro Karamatsu, Daisuke Harada, Kota Yamaguchi, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   1041 - 1046   2019.9

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    DOI： 10.1109/ICDAR.2019.00170

  • Page segmentation using a convolutional neural network with trainable co-occurrence features Reviewed International journal

    Joonho Lee, Hideaki Hayashi, Wataru Ohyama, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   1023 - 1028   2019.9

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    DOI： 10.1109/ICDAR.2019.00167

  • Logo design analysis by ranking Reviewed International journal

    Takuro Karamatsu, Daiki Suehiro, Seiichi Uchida

    15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019   1482 - 1487   2019.9

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    DOI： 10.1109/ICDAR.2019.00238

  • Biosignal Generation and Latent Variable Analysis With Recurrent Generative Adversarial Networks. Reviewed

    Shota Harada, Hideaki Hayashi, Seiichi Uchida

    IEEE Access   7   144292 - 144302   2019.8

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    DOI： 10.1109/ACCESS.2019.2934928

  • Scribbles for Metric Learning in Histological Image Segmentation. Reviewed

    Daisuke Harada, Ryoma Bise, Hiroki Tokunaga, Wataru Ohyama, Sanae Oka, Toshihiko Fujimori, Seiichi Uchida

    Proceedings of the 41st International Engineering in Medicine and Biology Conference (EMBC2019, Berlin, Germany)   1026 - 1030   2019.7

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    DOI： 10.1109/EMBC.2019.8856465

  • Endoscopic Image Clustering with Temporal Ordering Information Based on Dynamic Programming. Reviewed

    Shota Harada, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Qier Meng, Seiichi Uchida

    Proceedings of the 41st International Engineering in Medicine and Biology Conference (EMBC2019, Berlin, Germany)   3681 - 3684   2019.7

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    DOI： 10.1109/EMBC.2019.8857011

  • ProbAct: A Probabilistic Activation Function for Deep Neural Networks Reviewed

    Joonho Lee, Kumar Shridhar, Hideaki Hayashi, Brian Kenji Iwana, Seokjun Kang, Seiichi Uchida

    CoRR   abs/1905.10761   2019.5

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  • Prewarping Siamese Network: Learning Local Representations for Online Signature Verification. Reviewed

    Xiaomeng Wu, Akisato Kimura, Seiichi Uchida, Kunio Kashino

    Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019, Brighton, UK)   2467 - 2471   2019.5

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    DOI： 10.1109/ICASSP.2019.8683036

  • Dynamic Weight Alignment for Temporal Convolutional Neural Networks. Reviewed

    Brian Kenji Iwana, Seiichi Uchida

    Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019, Brighton, UK)   3827 - 3831   2019.5

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    DOI： 10.1109/ICASSP.2019.8682908

  • Scene word recognition from pieces to whole. Reviewed

    Anna Zhu, Seiichi Uchida

    Frontiers Comput. Sci.   13 ( 2 )   292 - 301   2019.4

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    Scene word recognition from pieces to whole.

    DOI： 10.1007/s11704-017-6420-2

  • Comic Text Detection Using Neural Network Approach. Reviewed

    MultiMedia Modeling - 25th International Conference, MMM 2019, Thessaloniki, Greece, January 8-11, 2019, Proceedings, Part II   672 - 683   2019.1

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    Comic Text Detection Using Neural Network Approach.

    DOI： 10.1007/978-3-030-05716-9_60

  • Efficient Soft-Constrained Clustering for Group-Based Labeling Reviewed International journal

    Ryoma Bise, Kentaro Abe, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida

    421 - 430   2019.1

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    DOI： 10.1007/978-3-030-32254-0_47

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed International journal

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    DOI： 10.1186/s40645-018-0245-y

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    DOI： 10.1186/s40645-018-0245-y

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed International journal

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    Language：English  

    DOI： 10.1186/s40645-018-0245-y

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed International journal

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    Language：English  

    DOI： 10.1186/s40645-018-0245-y

  • A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction. Reviewed

    Hideaki Hayashi, Seiichi Uchida

    Computer Vision - ACCV 2018 - 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part II   414 - 430   2018.12

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    A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction.

    DOI： 10.1007/978-3-030-20890-5_27

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed International journal

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    Language：English  

    DOI： 10.1186/s40645-018-0245-y

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed International journal

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    Language：English  

    DOI： 10.1186/s40645-018-0245-y

  • Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model Reviewed International journal

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama, Seiichi Uchida

    Progress in Earth and Planetary Science   5 ( 1 )   2018.12

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    Language：English  

    DOI： 10.1186/s40645-018-0245-y

  • Anatomical location classification of gastroscopic images using DenseNet trained from Cyclical Learning Rate

    Qier Meng, Kiyohito Tanaka, Shin'ichi Satoh, Masaru Kitsuregawa, Yusuke Kurose, Tatsuya Harada, Hideaki Hayashi, Ryoma Bise, Seiichi Uchida, Masahiro Oda, Kensaku Mori

    MIRU2018   PS1-51   2018.8

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    Anatomical location classification of gastroscopic images using DenseNet trained from Cyclical Learning Rate

  • On Fast Sample Preselection for Speeding up Convolutional Neural Network Training. Reviewed

    Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings   65 - 75   2018.8

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    On Fast Sample Preselection for Speeding up Convolutional Neural Network Training.

    DOI： 10.1007/978-3-319-97785-0_7

  • Introducing Local Distance-Based Features to Temporal Convolutional Neural Networks. Reviewed

    Brian Kenji Iwana, Minoru Mori, Akisato Kimura, Seiichi Uchida

    16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, Niagara Falls, NY, USA, August 5-8, 2018   92 - 97   2018.8

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    Introducing Local Distance-Based Features to Temporal Convolutional Neural Networks.

    DOI： 10.1109/ICFHR-2018.2018.00025

  • How do Convolutional Neural Networks Learn Design? Reviewed

    Shailza Jolly, Brian Kenji Iwana, Ryohei Kuroki, Seiichi Uchida

    24th International Conference on Pattern Recognition, ICPR 2018, Beijing, China, August 20-24, 2018   1085 - 1090   2018.8

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    How do Convolutional Neural Networks Learn Design?

    DOI： 10.1109/ICPR.2018.8545624

  • Discovering Class-Wise Trends of Max-Pooling in Subspace. Reviewed

    Yuchen Zheng, Brian Kenji Iwana, Seiichi Uchida

    16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, Niagara Falls, NY, USA, August 5-8, 2018   98 - 103   2018.8

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    Discovering Class-Wise Trends of Max-Pooling in Subspace.

    DOI： 10.1109/ICFHR-2018.2018.00026

  • An Image-Based Representation for Graph Classification. Reviewed

    Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings   140 - 149   2018.8

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    An Image-Based Representation for Graph Classification.

    DOI： 10.1007/978-3-319-97785-0_14

  • The cytoplasmic region of the amyloid β-protein precursor (APP) is necessary and sufficient for the enhanced fast velocity of APP transport by kinesin-1. Reviewed International journal

    592 ( 16 )   2716 - 2724   2018.8

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    DOI： 10.1002/1873-3468.13204

  • Biosignal Data Augmentation Based on Generative Adversarial Networks. Reviewed

    Shota Haradal, Hideaki Hayashi, Seiichi Uchida

    40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA, July 18-21, 2018   368 - 371   2018.7

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    Biosignal Data Augmentation Based on Generative Adversarial Networks.

    DOI： 10.1109/EMBC.2018.8512396

  • CNN Training with Graph-Based Sample Preselection: Application to Handwritten Character Recognition. Reviewed

    13th IAPR International Workshop on Document Analysis Systems, DAS 2018, Vienna, Austria, April 24-27, 2018   19 - 24   2018.4

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    CNN Training with Graph-Based Sample Preselection: Application to Handwritten Character Recognition.

    DOI： 10.1109/DAS.2018.10

  • Text Line Extraction Based on Integrated K-Shortest Paths Optimization. Reviewed

    Liuan Wang, Jun Su, Seiichi Uchida

    13th IAPR International Workshop on Document Analysis Systems, DAS 2018, Vienna, Austria, April 24-27, 2018   85 - 90   2018.4

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    Text Line Extraction Based on Integrated K-Shortest Paths Optimization.

    DOI： 10.1109/DAS.2018.68

  • Contained Neural Style Transfer for Decorated Logo Generation. Reviewed

    Gantugs Atarsaikhan, Brian Kenji Iwana, Seiichi Uchida

    13th IAPR International Workshop on Document Analysis Systems, DAS 2018, Vienna, Austria, April 24-27, 2018   317 - 322   2018.4

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    Contained Neural Style Transfer for Decorated Logo Generation.

    DOI： 10.1109/DAS.2018.78

  • Human Reading Knowledge Inspired Text Line Extraction Reviewed

    Liuan Wang, Seiichi Uchida, Anna Zhu, Jun Sun

    Cognitive Computation   10 ( 1 )   84 - 93   2018.2

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    DOI： 10.1007/s12559-017-9490-4

  • Component Awareness in Convolutional Neural Networks Reviewed

    Brian Kenji Iwana, Letao Zhou, Kumiko Tanaka-Ishii, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1   394 - 399   2018.1

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    DOI： 10.1109/ICDAR.2017.72

  • Scene Text Relocation with Guidance Reviewed

    Anna Zhu, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1   1289 - 1294   2018.1

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    DOI： 10.1109/ICDAR.2017.212

  • Scene Text Eraser Reviewed

    Toshiki Nakamura, Anna Zhu, Keiji Yanai, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1   832 - 837   2018.1

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    DOI： 10.1109/ICDAR.2017.141

  • Neural Font Style Transfer Reviewed

    Gantugs Atarsaikhan, Brian Kenji Iwana, Atsushi Narusawa, Keiji Yanai, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   5   51 - 56   2018.1

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    DOI： 10.1109/ICDAR.2017.328

  • How Does a CNN Manage Different Printing Types? Reviewed

    Shota Ide, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1   1004 - 1009   2018.1

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    DOI： 10.1109/ICDAR.2017.167

  • 傾斜文字認識のための正規化方法 Reviewed

    志久 修, 手島裕詞, 内田誠一

    電子情報通信学会論文誌   2017.11

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  • Globally Optimal Object Tracking with Complementary Use of Single Shot Multibox Detector and Fully Convolutional Network Reviewed

    Jinho Lee, Brian Kenji Iwana, Shouta Ide, Hideaki Hayashi, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   10749   110 - 122   2017.11

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    DOI： 10.1007/978-3-319-75786-5_10

  • Font Creation Using Class Discriminative Deep Convolutional Generative Adversarial Networks. Reviewed

    Kotaro Abe, Brian Kenji Iwana, Viktor, Gosta Holmer, Seiichi Uchida

    4th IAPR Asian Conference on Pattern Recognition, ACPR 2017, Nanjing, China, November 26-29, 2017   232 - 237   2017.11

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    Font Creation Using Class Discriminative Deep Convolutional Generative Adversarial Networks.

    DOI： 10.1109/ACPR.2017.99

  • Font Creation Using Generative Adversarial Networks with Class Discrimination Reviewed International journal

    Proceedings of Asian Conference on Pattern Recognition (ACPR2017, Nanjing, China)   2017.10

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  • Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes Reviewed

    Brian Kenji Iwana, Volkmar Frinkena, Kaspar Riesen, Seiichi Uchida

    PATTERN RECOGNITION   64   268 - 276   2017.4

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    DOI： 10.1016/j.patcog.2016.11.013

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    DOI： 10.1371/journal.pone.0175059

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    DOI： 10.1371/journal.pone.0175059

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    DOI： 10.1371/journal.pone.0175059

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    DOI： 10.1371/journal.pone.0175059

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    Language：English  

    DOI： 10.1371/journal.pone.0175059

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    DOI： 10.1371/journal.pone.0175059

  • Three-dimensional computer graphic animations for studying social approach behaviour in medaka fish Effects of systematic manipulation of morphological and motion cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PloS one   12 ( 4 )   2017.4

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    DOI： 10.1371/journal.pone.0175059

  • Three-Dimensional Computer Graphic Animations for Studying Social Approach Behaviour in Medaka Fish: Effects of Systematic Manipulation of Morphological and Motion Cues Reviewed International journal

    Tomohiro Nakayasu, Masaki Yasugi, Soma Shiraishi, Seiichi Uchida, Eiji Watanabe

    PLoS ONE   2017.4

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  • Reading-life log as a new paradigm of utilizing character and document media Reviewed

    Koichi Kise, Shinichiro Omachi, Seiichi Uchida, Masakazu Iwamura, Masahiko Inami, Kai Kunze

    Human-Harmonized Information Technology   2   197 - 233   2017.4

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    DOI： 10.1007/978-4-431-56535-2_7

  • Endoplasmic-reticulum-mediated microtubule alignment governs cytoplasmic streaming Reviewed

    Kenji Kimura, Alexandre Mamane, Tohru Sasaki, Kohta Sato, Jun Takagi, Ritsuya Niwayama, Lars Hufnagel, Yuta Shimamoto, Jean-Francois Joanny, Seiichi Uchida, Akatsuki Kimura

    NATURE CELL BIOLOGY   19 ( 4 )   399 - +   2017.4

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    DOI： 10.1038/ncb3490

  • A Preselection-Based Fast Support Vector Machine Learning for Large-Scale Pattern Sets using Compressed Relative Neighborhood Graph Reviewed

    Masanori Goto, Ryosuke Ishida, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   22 ( 1 )   1 - 7   2017.1

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  • Deep Learning-based Prediction Method for People Flows and Their Anomalies Reviewed

    Takano, Shigeru, Hori, Maiya, Goto, Takayuki, Uchida, Seiichi, Kurazume, Ryo, Taniguchi, Rin-ichiro

    ICPRAM: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS   676 - 683   2017.1

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    DOI： 10.5220/0006248806760683

  • Basal filopodia and vascular mechanical stress organize fibronectin into pillars bridging the mesoderm-endoderm gap Reviewed

    Yuki Sato, Kei Nagatoshi, Ayumi Hamano, Yuko Imamura, David Huss, Seiichi Uchida, Rusty Lansford

    Development (Cambridge)   144 ( 2 )   281 - 291   2017.1

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    DOI： 10.1242/dev.141259

  • What Does Scene Text Tell Us? Reviewed

    Seiichi Uchida, Yuto Shinahara

    2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)   4047 - 4052   2016.12

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    DOI： 10.1109/ICPR.2016.7900267

  • Could scene context be beneficial for scene text detection? Reviewed

    Anna Zhu, Renwu Gao, Seiichi Uchida

    PATTERN RECOGNITION   58   204 - 215   2016.10

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    DOI： 10.1016/j.patcog.2016.04.011

  • A Robust Dissimilarity-based Neural Network for Temporal Pattern Recognition Reviewed

    Brian Kenji Iwana, Volkmar Frinken, Seiichi Uchida

    PROCEEDINGS OF 2016 15TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR)   265 - 270   2016.10

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    DOI： 10.1109/ICFHR.2016.53

  • A Further Step to Perfect Accuracy by Training CNN with Larger Data Reviewed

    Seiichi Uchida, Shota Ide, Brian Kenji Iwana, Anna Zhu

    PROCEEDINGS OF 2016 15TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR)   405 - 410   2016.10

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    DOI： 10.1109/ICFHR.2016.77

  • Extraction and tracking living cells in medical images Reviewed

    O. Nedzvedz, S. Ablameyko, S. Uchida

    IDT 2016 - Proceedings of the International Conference on Information and Digital Technologies 2016   198 - 202   2016.8

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    DOI： 10.1109/DT.2016.7557173

  • Interphase adhesion geometry is transmitted to an internal regulator for spindle orientation via caveolin-1 Reviewed

    Shigeru Matsumura, Tomoko Kojidani, Yuji Kamioka, Seiichi Uchida, Tokuko Haraguchi, Akatsuki Kimura, Fumiko Toyoshima

    NATURE COMMUNICATIONS   7   ncomms11858   2016.6

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    DOI： 10.1038/ncomms11858

  • A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data Reviewed

    Markus Goldstein, Seiichi Uchida

    PLOS ONE   11 ( 4 )   e0152173   2016.4

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    DOI： 10.1371/journal.pone.0152173

  • Globally Optimal Text Line Extraction based on K-Shortest Paths algorithm Reviewed

    Liuan Wang, Wei Fan, Jun Sun, Seiichi Uchida

    PROCEEDINGS OF 12TH IAPR WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, (DAS 2016)   335 - 339   2016.4

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    DOI： 10.1109/DAS.2016.12

  • A RhoA and Rnd3 cycle regulates actin reassembly during membrane blebbing Reviewed

    Kana Aoki, Fumiyo Maeda, Tomoya Nagasako, Yuki Mochizuki, Seiichi Uchida, Junichi Ikenouchi

    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA   113 ( 13 )   E1863 - E1871   2016.3

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    DOI： 10.1073/pnas.1600968113

  • A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection Reviewed International journal

    Markus Goldstein, Seiichi Uchida

    Proceedings of The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM2016)   263 - 269   2016.2

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  • A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection Reviewed International journal

    Markus Goldstein, Seiichi Uchida

    Proceedings of The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM2016)   263 - 269   2016.2

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  • A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection Reviewed International journal

    Markus Goldstein, Seiichi Uchida

    Proceedings of The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM2016)   263 - 269   2016.2

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  • A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection Reviewed International journal

    Markus Goldstein, Seiichi Uchida

    Proceedings of The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM2016)   263 - 269   2016.2

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  • A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection Reviewed International journal

    Markus Goldstein, Seiichi Uchida

    Proceedings of The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM2016)   263 - 269   2016.2

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  • A Comparative Study on Outlier Removal from a Large-scale Dataset using Unsupervised Anomaly Detection Reviewed International journal

    Markus Goldstein, Seiichi Uchida

    Proceedings of The 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM2016)   263 - 269   2016.2

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  • A new method for multi-oriented graphics-scene-3D text classification in video Reviewed

    Jiamin Xu, Palaiahnakote Shivakumara, Tong Lu, Chew Lim Tan, Seiichi Uchida

    PATTERN RECOGNITION   49   19 - 42   2016.1

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    DOI： 10.1016/j.patcog.2015.07.002

  • Efficient Anchor Graph Hashing with Data-Dependent Anchor Selection Reviewed

    Hiroaki Takebe, Yusuke Uehara, Seiichi Uchida

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E98D ( 11 )   2030 - 2033   2015.11

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    DOI： 10.1587/transinf.2015EDL8060

  • Deep BLSTM Neural Networks for Unconstrained Continuous Handwritten Text Recognition Reviewed

    Volkmar Frinken, Seiichi Uchida

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   911 - 915   2015.8

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    DOI： 10.1109/ICDAR.2015.7333894

  • True Color Distributions of Scene Text and Background Reviewed

    Renwu Gao, Shoma Eguchi, Seiichi Uchida

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   506 - 510   2015.8

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    DOI： 10.1109/ICDAR.2015.7333813

  • Tackling Temporal Pattern Recognition by Vector Space Embedding Reviewed

    Brian Iwana, Seiichi Uchida, Kaspar Riesen, Volkmar Frinken

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   816 - 820   2015.8

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    DOI： 10.1109/ICDAR.2015.7333875

  • Similarity-based Regularization for Semi-Supervised Learning for Handwritten Digit Recognition Reviewed

    D. Barbuzzi, G. Pirlo, S. Uchida, V. Frinken, D. Impedovo

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   101 - 105   2015.8

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    DOI： 10.1109/ICDAR.2015.7333734

  • Preselection of Support Vector Candidates by Relative Neighborhood Graph for Large-Scale Character Recognition Reviewed

    Masanori Goto, Ryosuke Ishida, Seiichi Uchidat

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   306 - 310   2015.8

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    DOI： 10.1109/ICDAR.2015.7333773

  • Learning Non-Markovian Constraints for Handwriting Recognition Reviewed

    Ryosuke Kakisako, Seiichi Uchida, Frinken Volkmar

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   446 - 450   2015.8

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    DOI： 10.1109/ICDAR.2015.7333801

  • ICDAR 2015 Competition on Robust Reading Reviewed

    Dimosthenis Karatzas, Lluis Gomez-Bigorda, Anguelos Nicolaou, Suman Ghosh, Andrew Bagdanov, Masakazu Iwamura, Jiri Matas, Lukas Neumann, Vijay Ramaseshan Chandrasekhar, Shijian Lu, Faisal Shafait, Seiichi Uchida, Ernest Valveny

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   1156 - 1160   2015.8

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    DOI： 10.1109/ICDAR.2015.7333942

  • Exploring the World of Fonts for Discovering the Most Standard Fonts and the Missing Fonts Reviewed

    Seiichi Uchida, Yuji Egashira, Kota Sato

    2015 13TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   441 - 445   2015.8

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    DOI： 10.1109/ICDAR.2015.7333800

  • 付加情報の一般的な割り当て Reviewed

    岩村 雅一, 古谷 嘉男, 黄瀬 浩一, 大町 真一郎, 内田 誠一

    電子情報通信学会論文誌D   J93-D ( 5 )   579 - 587   2015.5

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    特徴量のみでは本質的に避けることができない誤認識を回避するために， 付加情報を用いるパターン認識という枠組みが提案されている． この方式では，パターン認識を行う際に， 付加情報と呼ばれるクラスの決定を補助する少量の情報を特徴量と 同時に用いて認識性能の改善を目指す． 付加情報は自由に設定でき，通常は誤認識率が最小になるように設定する． ここで問題となるのは，誤認識率が最小になる付加情報の設定方法である． 常に正しい付加情報が得られるいう理想的な条件においては 既に問題が定式化され，付加情報の割り当て方法が導かれている． しかし，実環境での使用を考えると， 付加情報に生じる観測誤差を考慮した割り当て方法が求められる． そこで本論文では 付加情報の観測誤差を考慮に入れて，問題を新たに定式化する． これは付加情報が誤らない場合にも有効な一般的なものである． 本論文で導いた割り当て方法が有効に機能することを マハラノビス距離を用いた実験で例示する．

  • Improving Hausdorff edit distance using structural node context Reviewed

    Andreas Fischer, Seiichi Uchida, Volkmar Frinken, Kaspar Riesen, Horst Bunke

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   9069   148 - 157   2015.5

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    DOI： 10.1007/978-3-319-18224-7_15

  • Data Embedding into Characters Reviewed

    Koichi Kise, Shinichiro Omachi, Seiichi Uchida, Masakazu Iwamura, Marcus Liwicki

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E98D ( 1 )   10 - 20   2015.1

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    DOI： 10.1587/transinf.2014MUI0002

  • Visual Saliency Models for Text Detection in Real World Reviewed

    Renwu Gao, Seiichi Uchida, Asif Shahab, Faisal Shafait, Volkmar Frinken

    PLOS ONE   9 ( 12 )   e114539   2014.12

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    DOI： 10.1371/journal.pone.0114539

  • Quantitative analysis of APP axonal transport in neurons: role of JIP1 in enhanced APP anterograde transport. Reviewed International journal

    Kyoko Chiba, Masahiko Araseki, Keisuke Nozawa, Keiko Furukori, Yoichi Araki, Takahide Matsushima, Tadashi Nakaya, Saori Hata, Yuhki Saito, Seiichi Uchida, Yasushi Okada, Angus C Nairn, Roger J Davis, Tohru Yamamoto, Masataka Kinjo, Hidenori Taru, Toshiharu Suzuki

    Molecular biology of the cell   25 ( 22 )   3569 - 80   2014.11

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    DOI： 10.1091/mbc.E14-06-1111

  • Comparative performance analysis of stroke correspondence search methods for stroke-order free online multi-stroke character recognition Reviewed

    Wenjie Cai, Seiichi Uchida, Hiroaki Sakoe

    FRONTIERS OF COMPUTER SCIENCE   8 ( 5 )   773 - 784   2014.10

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    DOI： 10.1007/s11704-014-3207-6

  • A Novel HMM Decoding Algorithm Permitting Long-Term Dependencies and its Application to Handwritten Word Recognition Reviewed

    Volkmar Frinken, Ryosuke Kakisako, Seiichi Uchida

    2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR)   128 - 133   2014.9

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    DOI： 10.1109/ICFHR.2014.29

  • Constrained AdaBoost for Totally-Ordered Global Features Reviewed

    Ryota Ogata, Minoru Mori, Volkmar Frinken, Seiichi Uchida

    2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR)   393 - 398   2014.9

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    DOI： 10.1109/ICFHR.2014.72

  • Automatic Signature Stability Analysis And Verification Using Local Features Reviewed

    Muhammad Imran Malik, Marcus Liwicki, Andreas Dengel, Seiichi Uchida, Volkmar Frinken

    2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR)   621 - 626   2014.9

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    DOI： 10.1109/ICFHR.2014.109

  • Improved BLSTM Neural Networks for Recognition of On-Line Bangla Complex Words Reviewed

    Volkmar Frinken, Nilanjana Bhattacharya, Seiichi Uchida, Umapada Pal

    STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION   8621   404 - 413   2014.8

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    DOI： 10.1007/978-3-662-44415-3_41

  • Selective Concealment of Characters for Privacy Protection Reviewed

    Kohei Inai, Marten Palsson, Volkmar Frinken, Yaokai Feng, Seiichi Uchida

    2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)   333 - 338   2014.8

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    DOI： 10.1109/ICPR.2014.66

  • LSTM-Based Early Recognition of Motion Patterns Reviewed

    Markus Weber, Marcus Liwicki, Didier Stricker, Christopher Schoelzel, Seiichi Uchida

    2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)   3552 - 3557   2014.8

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    DOI： 10.1109/ICPR.2014.611

  • Improving Point of View Scene Recognition by Considering Textual Data Reviewed

    Volkmar Frinken, Yutaro Iwakiri, Ryosuke Ishida, Kensho Fujisaki, Seiichi Uchida

    2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)   2966 - 2971   2014.8

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    DOI： 10.1109/ICPR.2014.512

  • Global feature for online character recognition Reviewed

    Minoru Mori, Seiichi Uchida, Hitoshi Sakano

    PATTERN RECOGNITION LETTERS   35   142 - 148   2014.1

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    DOI： 10.1016/j.patrec.2013.03.036

  • Text localization and recognition in images and video Reviewed

    Seiichi Uchida

    Handbook of Document Image Processing and Recognition   843 - 883   2014.1

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    DOI： 10.1007/978-0-85729-859-1_28

  • Simple and Direct Assembly of Kymographs from Movies Using KYMOMAKER Reviewed

    Kyoko Chiba, Yuki Shimada, Masataka Kinjo, Toshiharu Suzuki, Seiich Uchida

    TRAFFIC   15 ( 1 )   1 - 11   2014.1

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    DOI： 10.1111/tra.12127

  • Recovery and localization of handwritings by a camera-pen based on tracking and document image retrieval Reviewed

    Megumi Chikano, Koichi Kise, Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi

    PATTERN RECOGNITION LETTERS   35   214 - 224   2014.1

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.patrec.2012.10.003

  • More than ink - Realization of a data-embedding pen Reviewed

    Marcus Liwicki, Seiichi Uchida, Akira Yoshida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    PATTERN RECOGNITION LETTERS   35   246 - 255   2014.1

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    DOI： 10.1016/j.patrec.2012.09.001

  • A parallel image encryption method based on compressive sensing Reviewed

    R. Huang, K. H. Rhee, S. Uchida

    Multimedia Tools and Applications   72 ( 1 )   71 - 93   2013.12

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    DOI： 10.1007/s11042-012-1337-0

  • Stable Marriage Algorithm for Tracking Intracellular Objects Reviewed

    Ayumi Hamano, Kensho Fujisaki, Seiichi Uchida, Osamu Shiku

    2013 FIRST INTERNATIONAL SYMPOSIUM ON COMPUTING AND NETWORKING (CANDAR)   305 - 307   2013.12

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    DOI： 10.1109/CANDAR.2013.53

  • Detection and Tracking Protein Molecules in Fluorescence Microscopic Video Reviewed

    Kensho Fujisaki, Ayumi Hamano, Kenta Aoki, Yaokai Feng, Seiichi Uchida, Masahiko Araseki, Yuki Saito, Toshiharu Suzuki

    2013 FIRST INTERNATIONAL SYMPOSIUM ON COMPUTING AND NETWORKING (CANDAR)   270 - 274   2013.12

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    DOI： 10.1109/CANDAR.2013.47

  • A Voting-Based Sequential Pattern Recognition Method Reviewed

    Koichi Ogawara, Masahiro Fukutomi, Seiichi Uchida, Yaokai Feng

    PLOS ONE   8 ( 10 )   e76980   2013.10

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    DOI： 10.1371/journal.pone.0076980

  • Scene Character Detection and Recognition with Cooperative Multiple-Hypothesis Framework Reviewed

    Rong Huang, Palaiahnakote Shivakumara, Yaokai Feng, Seiichi Uchida

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E96D ( 10 )   2235 - 2244   2013.10

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    DOI： 10.1587/transinf.E96.D.2235

  • Activity Recognition for the Mind: Toward a Cognitive "Quantified Self" Reviewed

    Kai Kunze, Masakazu Iwamura, Koichi Kise, Seiichi Uchida, Shinichiro Omachi

    COMPUTER   46 ( 10 )   105 - 108   2013.10

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    DOI： 10.1109/MC.2013.339

  • An Efficient Radical-Based Algorithm for Stroke-Order Free and Stroke-Number Free Online Kanji Character Recognition Reviewed International journal

    Wenjie Cai, Seiichi Uchida and Hiroaki Sakoe

    Proceedings of the 16th International Graphonomics Society Conference (IGS 2013, Nara, Japan)   82.0 - 85.0   2013.8

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  • The Reading-life Log - Technologies to Recognize Texts That We Read Reviewed

    Takashi Kimura, Rong Huang, Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   91 - 95   2013.8

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    DOI： 10.1109/ICDAR.2013.26

  • Scene Character Detection by an Edge-Ray Filter Reviewed

    Rong Huang, Palaiahnakote Shivakumara, Seiichi Uchida

    2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   462 - 466   2013.8

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    DOI： 10.1109/ICDAR.2013.99

  • Part-Based Recognition of Arbitrary Fonts Reviewed

    Wang Song, Seiichi Uchida, Marcus Liwicki

    2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   170 - 174   2013.8

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    DOI： 10.1109/ICDAR.2013.41

  • On the Possibility of Structure Learning-Based Scene Character Detector Reviewed

    Yugo Terada, Rong Huang, Yaokai Feng, Seiichi Uchida

    2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   472 - 476   2013.8

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    DOI： 10.1109/ICDAR.2013.101

  • ICDAR 2013 Robust Reading Competition Reviewed

    Dimosthenis Karatzas, Faisal Shafait, Seiichi Uchida, Masakazu Iwamura, Lluis Gomez i Bigorda, Sergi Robles Mestre, Joan Mas, David Fernandez Mota, Jon Almazan Almazan, Lluis Pere de las Heras

    2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   1484 - 1493   2013.8

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    DOI： 10.1109/ICDAR.2013.221

  • Font Distribution Observation by Network-Based Analysis Reviewed

    Chihiro Nakamoto, Rong Huang, Sota Koizumi, Ryosuke Ishida, Yaokai Feng, Seiichi Uchida

    CAMERA-BASED DOCUMENT ANALYSIS AND RECOGNITION, CBDAR 2013   8357   83 - 97   2013.8

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    DOI： 10.1007/978-3-319-05167-3_7

  • Analyzing the Distribution of a Large-scale Character Pattern Set Using Relative Neighborhood Graph Reviewed

    Masanori Goto, Ryosuke Ishida, Yaokai Feng, Seiichi Uchida

    2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR)   3 - 7   2013.8

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    DOI： 10.1109/ICDAR.2013.10

  • A Hierarchical Visual Saliency Model for Character Detection in Natural Scenes Reviewed

    Renwu Gao, Faisal Shafait, Seiichi Uchida, Yaokai Feng

    CAMERA-BASED DOCUMENT ANALYSIS AND RECOGNITION, CBDAR 2013   8357   18 - 29   2013.8

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    DOI： 10.1007/978-3-319-05167-3_2

  • Saliency inside Saliency - A Hierarchical Usage of Visual Saliency for Scene Character Detection Reviewed International journal

    Renwu Gao, Faisal Shafait, Seiichi Uchida, Yaokai Feng

    Proceedings of The 12th International Conference on Document Analysis and Recognition (ICDAR 2013, Washington DC, USA)   2013.8

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  • Part-based methods for handwritten digit recognition Reviewed

    Song Wang, Seiichi Uchida, Marcus Liwicki, Yaokai Feng

    FRONTIERS OF COMPUTER SCIENCE   7 ( 4 )   514 - 525   2013.8

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    DOI： 10.1007/s11704-013-2297-x

  • Font Distribution Analysis by Network Reviewed International journal

    Chihiro Nakamoto, Rong Huang, Sota Koizumi, Ryosuke Ishida, Yaokai Feng and Seiichi Uchida

    Proceedings of The 12th International Conference on Document Analysis and Recognition (ICDAR 2013, Washington DC, USA)   2013.8

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  • Exploring the Ability of Parts on Recognizing Handwriting Characters Reviewed International journal

    Takafumi Matsuo, Song Wang, Yaokai Feng and Seiichi Uchida

    Proceedings of the 16th International Graphonomics Society Conference (IGS 2013, Nara, Japan)   66.0 - 69.0   2013.8

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  • Skew Estimation by Parts Reviewed

    Soma Shiraishi, Yaokai Feng, Seiichi Uchida

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E96D ( 7 )   1503 - 1512   2013.7

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    DOI： 10.1587/transinf.E96.D.1503

  • A Proposal of Writing-Life Log and Its Implementation Using a Retrieval-Based Camera-Pen Reviewed

    Koichi Kise, Riki Kudo, Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi

    Proceedings of the 16th International Graphonomics Society Conference (IGS 2013)   86 - 89   2013.6

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    A Proposal of Writing-Life Log and Its Implementation Using a Retrieval-Based Camera-Pen

  • Image processing and recognition for biological images Reviewed

    Seiichi Uchida

    DEVELOPMENT GROWTH & DIFFERENTIATION   55 ( 4 )   523 - 549   2013.5

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    DOI： 10.1111/dgd.12054

  • Non-Markovian Dynamic Time Warping Reviewed

    Seiichi Uchida, Masahiro Fukutomi, Koichi Ogawara, Yaokai Feng

    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)   2294 - 2297   2012.11

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  • Scene Character Detection and Recognition Based on Multiple Hypotheses Framework Reviewed

    Rong Huang, Shinpei Oba, Shivakumara Palaiahnakote, Seiichi Uchida

    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)   717 - 720   2012.11

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  • Part-Based Method on Handwritten Texts Reviewed

    Song Wang, Seiichi Uchida, Marcus Liwicki

    2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)   339 - 342   2012.11

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  • Analytical Dynamic Programming Matching Reviewed

    Seiichi Uchida, Satoshi Hokahori, Yaokai Feng

    COMPUTER VISION - ECCV 2012: WORKSHOPS AND DEMONSTRATIONS, PT I   7583   92 - 101   2012.9

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    DOI： 10.1007/978-3-642-33863-2_10

  • On the Possibility of Instance-Based Stroke Recovery Reviewed

    Yutaro Iwakiri, Soma Shiraishi, Yaokai Feng, Seiichi Uchida

    13TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR 2012)   29 - 34   2012.9

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    DOI： 10.1109/ICFHR.2012.248

  • Dynamic Programming Matching with Global Features for Online Character Recognition Reviewed

    Minoru Mori, Seiichi Uchida, Hitoshi Sakano

    13TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR 2012)   348 - 353   2012.9

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    DOI： 10.1109/ICFHR.2012.199

  • Character Image Patterns as Big Data Reviewed

    Seiichi Uchida, Ryosuke Ishida, Akira Yoshida, Wenjie Cai, Yaokai Feng

    13TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR 2012)   479 - 484   2012.9

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    DOI： 10.1109/ICFHR.2012.190

  • Affine-invariant character recognition by progressive removing Reviewed

    Masakazu Iwamura, Akira Horimatsu, Ryo Niwa, Koichi Kise, Seiichi Uchida, Shinichiro Omachi

    ELECTRICAL ENGINEERING IN JAPAN   180 ( 2 )   55 - 63   2012.7

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    DOI： 10.1002/eej.22276

  • Fluorescence Sensing Film for Odor Imaging Reviewed International journal

    Y. Furusawa, M. Imanishi, S. Hirata, S. Uchida, K. Nakano, K. Hayashi

    Proceedings of the 6th Asia-Pacific Conference on Transducers and Micro/Nano Technologies   2012.7

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  • Message from general chair and program chairs Reviewed

    Michael Blumenstein, Umapada Pal, Seiichi Uchida

    10th IAPR International Workshop on Document Analysis Systems, DAS 2012 Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012   xii - xiii   2012.5

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    DOI： 10.1109/DAS.2012.55

  • A part-based skew estimation method Reviewed

    Soma Shiraishi, Yaokai Feng, Seiichi Uchida

    Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012   185 - 189   2012.3

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    DOI： 10.1109/DAS.2012.7

  • Toward part-based document image decoding Reviewed

    Wang Song, Seiichi Uchida, Marcus Liwicki

    Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012   266 - 270   2012.3

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    DOI： 10.1109/DAS.2012.90

  • How salient is scene text? Reviewed

    Asif Shahab, Faisal Shafait, Andreas Dengel, Seiichi Uchida

    Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012   317 - 321   2012.3

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    DOI： 10.1109/DAS.2012.42

  • How important is global structure for characters? Reviewed

    Minoru Mori, Seiichi Uchida, Hitoshi Sakano

    Proceedings - 10th IAPR International Workshop on Document Analysis Systems, DAS 2012   255 - 260   2012.3

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    DOI： 10.1109/DAS.2012.41

  • Optical odor imaging by fluorescence probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, Kenshi Hayashi

    Journal of Robotics and Mechatronics   24 ( 1 )   47 - 54   2012.2

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    DOI： 10.20965/jrm.2012.p0047

  • Optical odor imaging by fluorescence probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, Kenshi Hayashi

    Journal of Robotics and Mechatronics   24 ( 1 )   47 - 54   2012.2

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    DOI： 10.20965/jrm.2012.p0047

  • Optical odor imaging by fluorescence probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, Kenshi Hayashi

    Journal of Robotics and Mechatronics   24 ( 1 )   47 - 54   2012.2

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    DOI： 10.20965/jrm.2012.p0047

  • Optical odor imaging by fluorescence probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, Kenshi Hayashi

    Journal of Robotics and Mechatronics   24 ( 1 )   47 - 54   2012.2

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    DOI： 10.20965/jrm.2012.p0047

  • Optical odor imaging by fluorescence probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, Kenshi Hayashi

    Journal of Robotics and Mechatronics   24 ( 1 )   47 - 54   2012.2

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    DOI： 10.20965/jrm.2012.p0047

  • Optical odor imaging by fluorescence probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, Kenshi Hayashi

    Journal of Robotics and Mechatronics   24 ( 1 )   47 - 54   2012.2

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    DOI： 10.20965/jrm.2012.p0047

  • Part-Based Skew Estimation for Mathematical Expressions Reviewed International journal

    Soma Shiraishi, Yaokai Feng and Seiichi Uchida

    Proceedings of The International Workshop on "Digitization and E-Inclusion in Mathematics and Science 2012 (DEIMS12, Tokyo, Japan)   2012.2

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  • Optical Odor Imaging by Fluorescence Probes Reviewed International journal

    Hirotaka Matsuo, Yudai Furusawa, Masashi Imanishi, Seiichi Uchida, and Kenshi Hayashi

    Journal of Robotics and Mechatoronics   2012.1

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  • Toward Forensics by Stroke Order Variation - Performance Evaluation of Stroke Correspondence Methods Reviewed

    Wenjie Cai, Seiichi Uchida, Hiroaki Sakoe

    4th International Workshop, IWCF 2010 Tokyo, Japan, November 11-12, 2010, Revised Selected Papers   6540   43 - +   2011.11

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    DOI： 10.1007/978-3-642-19376-7_4

  • A Generative Model for Handwritings Based on Enhanced Feature Desynchronization Reviewed

    Seiichi Uchida, Toru Sasaki, Feng Yaokai

    11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011)   589 - 593   2011.9

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    DOI： 10.1109/ICDAR.2011.124

  • WATCHING PATTERN DISTRIBUTION VIA MASSIVE CHARACTER RECOGNITION Invited Reviewed

    Seiichi Uchida, Wenjie Cai, Akira Yoshida, Yaokai Feng

    2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)   1 - 6   2011.9

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    DOI： 10.1109/MLSP.2011.6064640

  • Scenery Character Detection with Environmental Context Reviewed

    Yasuhiro Kunishige, Feng Yaokai, Seiichi Uchida

    11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011)   1049 - 1053   2011.9

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    DOI： 10.1109/ICDAR.2011.212

  • Reliable Online Stroke Recovery from Offline Data with the Data-Embedding Pen Reviewed

    Marcus Liwicki, Yoshida Akira, Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011)   1384 - 1388   2011.9

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    DOI： 10.1109/ICDAR.2011.278

  • Look Inside the World of Parts of Handwritten Characters Reviewed

    Wang Song, Seiichi Uchida, Marcus Liwicki

    11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011)   784 - 788   2011.9

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    DOI： 10.1109/ICDAR.2011.161

  • Handwriting on paper as a cybermedium Reviewed

    Akira Yoshida, Marcus Liwichi, Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6884 ( 4 )   204 - 211   2011.9

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    DOI： 10.1007/978-3-642-23866-6_22

  • Comparative Study of Part-Based Handwritten Character Recognition Methods Reviewed

    Wang Song, Seiichi Uchida, Marcus Liwicki

    11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011)   814 - 818   2011.9

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    DOI： 10.1109/ICDAR.2011.167

  • A new approach for instance-based skew estimation Reviewed

    Soma Shiraishi, Yaokai Feng, Seiichi Uchida

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6884 ( 4 )   195 - 203   2011.9

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    DOI： 10.1007/978-3-642-23866-6_21

  • A Keypoint-Based Approach Toward Scenery Character Detection Reviewed

    Seiichi Uchida, Yuki Shigeyoshi, Yasuhiro Kunishige, Feng Yaokai

    11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011)   819 - 823   2011.9

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    DOI： 10.1109/ICDAR.2011.168

  • 段階的な枝刈りによるアフィン不変な文字認識

    岩村 雅一, 堀松 晃, 丹羽 亮, 黄瀬 浩一, 内田 誠一, 大町 真一郎

    電気学会論文誌. D, 産業応用部門誌 = The transactions of the Institute of Electrical Engineers of Japan. D, A publication of Industry Applications Society   131 ( 7 )   873 - 879   2011.7

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    Recognizing characters in scene images suffering from perspective distortion is a challenge. Although there are some methods to overcome this difficulty, they are time-consuming. In this paper, we propose a set of affine invariant features and a new recognition scheme called "progressive removing" that can help reduce the processing time. Progressive removing gradually removes less feasible categories and skew angles by using multiple classifiers. We observed that progressive removing and the use of the affine invariant features reduced the processing time by about 60% in comparison to a trivial one without decreasing the recognition rate.

    DOI： 10.1541/ieejias.131.873

  • Object Extraction at Nano-Surface Images Reviewed International journal

    A. Nedzved, O. Nedzved, Sergey Ablameyko, Seiichi Uchida

    Proceedings of The Eleventh International Conference on Pattern Recognition and Information Processing (PRIP2011, Minsk, Belarus)   2011.5

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  • RFIDを援用した映像中の人物追跡

    山田 興, 内田 誠一, 谷口 倫一郎

    電気学会論文誌. D, 産業応用部門誌 = The transactions of the Institute of Electrical Engineers of Japan. D, A publication of Industry Applications Society   131 ( 4 )   4 - 447   2011.4

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    This paper reports a new method for visual tracking of humans using active RFID technology. Previous studies were based on the assumption that the radio intensity from an RFID tag will be linearly proportional to the distance between the tag and the antenna or will remain unchanged; however, in reality, the intensity fluctuates significantly and changes drastically with a small change in the environment. The proposed method helps to overcome this problem by using only accurate binary information that reveals whether the target person is close to the antenna. Several experimental results have shown that the information from the RFID tag was useful for reliable tracking of humans.

    DOI： 10.1541/ieejias.131.441

  • Massive character recognition with a large ground-truthed database Reviewed

    Wenjie Cai, Yaokai Feng, Seiichi Uchida

    Proceedings of the ACM Symposium on Applied Computing   240 - 244   2011.3

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    DOI： 10.1145/1982185.1982241

  • Analytical Dynamic Programming Tracker Reviewed

    Seiichi Uchida, Ikko Fujimura, Hiroki Kawano, Yaokai Feng

    COMPUTER VISION-ACCV 2010, PT I   6492   296 - 309   2010.11

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    DOI： 10.1007/978-3-642-19315-6_23

  • Tracking and retrieval of pen tip positions for an intelligent camera pen Reviewed

    Kazumasa Iwata, Koichi Kise, Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi

    Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010   277 - 282   2010.11

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    DOI： 10.1109/ICFHR.2010.50

  • Part-based recognition of handwritten characters Reviewed

    Seiichi Uchida, Marcus Liwicki

    Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010   545 - 550   2010.11

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    DOI： 10.1109/ICFHR.2010.90

  • Embedding Meta-information in handwriting - Reed-solomon for reliable error correction Reviewed

    Marcus Liwicki, Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010   51 - 56   2010.11

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    DOI： 10.1109/ICFHR.2010.127

  • Automatic Construction of Gesture Network for Gesture Recognition Reviewed

    Akihiro Mori, Seiichi Uchida, Ryo Kurazume, Rin-ichiro Taniguchi, Tsutomu Hasegawa

    TENCON 2010: 2010 IEEE REGION 10 CONFERENCE   923 - 928   2010.11

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    DOI： 10.1109/TENCON.2010.5686549

  • Analysis of local features for handwritten character recognition Reviewed

    Seiichi Uchida, Marcus Liwicki

    Proceedings - International Conference on Pattern Recognition   1945 - 1948   2010.8

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    DOI： 10.1109/ICPR.2010.479

  • Hierarchical decomposition of handwriting deformation vector field for improving recognition accuracy Reviewed

    Toru Wakahara, Seiichi Uchida

    Proceedings - International Conference on Pattern Recognition   1860 - 1863   2010.8

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    DOI： 10.1109/ICPR.2010.459

  • Data-embedding pen

    Marcus Liwicki, Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the 8th IAPR International Workshop on Document Analysis Systems - DAS '10   2010.6

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    DOI： 10.1145/1815330.1815336

  • Expansion of queries and databases for improving the retrieval accuracy of document portions

    Koichi Kise, Megumi Chikano, Kazumasa Iwata, Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi

    Proceedings of the 8th IAPR International Workshop on Document Analysis Systems - DAS '10   2010.6

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    DOI： 10.1145/1815330.1815370

  • Grammatical verification for mathematical formula recognition based on context-free tree grammar Reviewed

    Akio Fujiyoshi, Masakazu Suzuki, Seiichi Uchida

    Mathematics in Computer Science   3 ( 3 )   279 - 298   2010.5

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    DOI： 10.1007/s11786-010-0023-8

  • 付加情報の一般的な割り当て Reviewed

    岩村雅一, 古谷嘉男, 黄瀬浩一, 大町真一郎, 内田誠一

    電子情報通信学会論文誌   2010.5

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  • 付加情報の一般的な割当(パターン認識)

    岩村 雅一, 古谷 嘉男, 黄瀬 浩一, 大町 真一郎, 内田 誠一

    電子情報通信学会論文誌. D, 情報・システム   93 ( 5 )   579 - 587   2010.5

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    特徴量のみでは本質的に避けることができない誤認識を回避するために,付加情報を用いるパターン認識という枠組みが提案されている.この方式では,パターン認識を行う際に,付加情報と呼ばれるクラスの決定を補助する少量の情報を特徴量と同時に用いて認議性能の改善を目指す.付加情報は自由に設定でき,通常は誤認識率が最小になるように設定する.ここで問題となるのは,誤認識率が最小になる付加情報の設定方法である.常に正しい付加情報が得られるいう理想的な条件においては既に問題が定式化され,付加情報の割当方法が導かれている.しかし,実環境での使用を考えると,付加情報に生じる観測誤差を考慮した割当方法が求められる.そこで本論文では付加情報の観測誤差を考慮に入れて,問題を新たに定式化する.これは付加情報が誤らない場合にも有効な一般的なものである.本論文で導いた割当方法が有効に機能することをマハラノビス距離を用いた実験で例示する.

  • 相互制約付き多数決型アルゴリズムによる時系列パターン認識

    福冨 正弘, 小川原 光一, 馮 尭楷, 内田 誠一

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   93 ( 4 )   548 - 551   2010.4

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    本論文では,時系列パターンの認識手法として,各サンプル点(各時刻)で認識すなわちクラスラベルの決定を行い,最終的にクラスラベル数の多数決によってクラスを確定する手法を検討する.その一つの特徴として,必要に応じて複数サンプル点間に相互制約を設け,それらをできるだけ同じクラスにラベリングする点が挙げられる.これにより,クラスラベルの割当方を制御でき,自由度の高い識別が可能となる.クラスラベルの割当の組合せは総サンプル点数に対し指数関数的に増加する.そこで,グラフの最小切断アルゴリズムいわゆるグラフカットを用いることで,総サンプル点数に対して多項式時間での計算を実現する.オンライン文字データを対象とした認識実験を行い,本手法の有効性を検証した.

  • 非線形有限要素解析を模したニューラルネットワークを用いた軟性臓器ボリュームモデルの変形シミュレータ

    諸岡 健一, 陳 献, 倉爪 亮, 内田 誠一, 原 健二, 砂川 賢二, 橋爪 誠

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   93 ( 3 )   365 - 376   2010.3

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    本論文では,ニューラルネットワークを用いて,軟性臓器モデルの変形をシミュレートする新たな手法を提案する.提案手法は,基本的なモデルの変形(以後,変形モードと呼ぶ)の組合せに基づいて,モデルの変形を推定する.つまり,変形モードをあらかじめ非線形有限要素法で求め,臓器に加わった外力と,それに対応する変形モードの関係をニューラルネットワークで学習する.学習したニューラルネットワークは,非線形有限要素解析によりモデルの振舞いを推定することを模倣する.実験結果より,提案手法は,非線形有限要素解析とほぼ同程度の精度を保ちつつ,計算コストを大幅に削減することができた.

  • ディジタルペン(技術解説) Invited

    内田 誠一, Marcus Liwicki, 岩村 雅一, 大町 真一郎, 黄瀬 浩一

    映像情報メディア学会誌 : 映像情報メディア   64 ( 3 )   293 - 298   2010.3

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    ディジタルペンについて概観する.ディジタルペンは,スムーズな文字・図形入力機能,および迅速なポインティング機能を持った,優れたインタフェースである.種類としては,筆記対象が限定されたもの,筆記対象が任意のものに大別される.本稿では,タブレットやアノトペンなどすでに製品化されている技術について述べ,今後の課題を考察する.

    DOI： 10.3169/itej.64.293

  • AdaBoost による気道・食道自動識別

    田村 暁斗, 諸岡 健一, 倉爪 亮, 岩下 友美, 内田 誠一, 原 健二, 中西 洋一, 橋爪 誠, 長谷川 勉

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   92 ( 12 )   2249 - 2260   2009.12

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    気道確保法の一つである気道挿管では,通常まず喉頭鏡を使って喉頭展開を行い,声門の位置を目視により確認する.しかし実際の医療現場では,上気道閉塞など様々な要因で,声門の位置を目視により確認しづらい場合がある.この不完全な確認が原因で食道へ誤挿管した場合,気道が確保されず危険なだけでなく,無理な目視のために頸椎や歯牙損傷などの合併症を引き起こす危険性がある.安全・確実な気道挿管の実現に向けて,我々は,スタイレット先端に小型カメラを搭載した自動気管内挿管システムを開発することを自指している.本論文では,その要素機能として,カメラから取得される画像から,挿管チューブが気道あるいは食道に挿管されているかを自動的に識別する方法を提案する.本手法は,気道画像には気道周囲の輪状軟骨が特徴的に観察されることから,まずこの環状模様の記述に適した特徴量を定義し,それに基づいた気道・食道識別器をAdaBoostによって構築する.実験の結果,97.6%の高い識別率で気道・食道の判別が可能であり,提案手法の有効性が確認できた.

  • Extract Baseline Information Using Support Vector Machine Reviewed International journal

    Walaa Aly, Seiichi Uchida and Masakazu Suzuki

    Proceedings of The 9th Asian Symposium on Computer Mathematics   2009.12

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  • Automatic Classification of Spatial Relationships among Mathematical Symbols Using Geometric Features Reviewed

    Walaa Aly, Seiichi Uchida, Masakazu Suzuki

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E92D ( 11 )   2235 - 2243   2009.11

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    DOI： 10.1587/transinf.E92.D.2235

  • 大局的最適化に基づくトラッキング : DPトラッキング(追跡・位置合わせ,第12回画像の認識・理解シンポジウム推薦論文,<特集>画像の認識・理解論文)

    藤村 一行, 内田 誠一

    電子情報通信学会論文誌. D, 情報・システム   92 ( 8 )   1279 - 1288   2009.8

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    映像中の物体のトラッキングは,その物体のフレーム間の移動量の最適推定問題として定式化される.本論文では,その大局的最適解を得るために,動的計画法(DP)を用いたトラッキング手法を提案する.従来,幅優先探索の一種として扱われていたDP最適化では,画像のサイズやパラメータの増加により,探索幅が非常に大きくなり計算量が増加するという問題がある.これに対し本論文ではDPの解析的解法をトラッキング問題に適用する.これは,最適化の評価に用いられる局所的な誤差関数を二次関数近似することで,DPによる最適化過程に微分による最適化を導入した手法である.幅優先探索なしに解析的にかつ高速に最適解を得ることができ,トラッキング問題には特に有効といえる.本論文では,本手法の定式化と実験結果を示す.

  • Document-Level Positioning of a Pen Tip by Retrieval of Image Fragments Reviewed

    Koichi Kise, Kazumasa Iwata, Tomohiro Nakai, Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi

    Proceedings of the Third International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2009)   61 - 68   2009.7

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    Document-Level Positioning of a Pen Tip by Retrieval of Image Fragments

  • Syntactic detection and correction of misrecognitions in mathematical OCR Reviewed

    Akio Fujiyoshi, Masakazu Suzuki, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1360 - 1364   2009.7

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    DOI： 10.1109/ICDAR.2009.150

  • Stochastic model of stroke order variation Reviewed

    Yoshinori Katayama, Seiichi Uchida, Hiroaki Sakoe

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   803 - 807   2009.7

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    DOI： 10.1109/ICDAR.2009.146

  • Statistical classification of spatial relationships among mathematical symbols Reviewed

    Walaa Aly, Seiichi Uchida, Akio Fujiyoshi, Masakazu Suzuki

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1350 - 1354   2009.7

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    DOI： 10.1109/ICDAR.2009.90

  • Hierarchical decomposition of handwriting deformation vector field using 2D warping and global/local affine transformation Reviewed

    Toru Wakahara, Seiichi Uchida

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1141 - 1145   2009.7

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    DOI： 10.1109/ICDAR.2009.33

  • Conspicuous character patterns Reviewed

    Seiichi Uchida, Ryoji Hattori, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   16 - 20   2009.7

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    DOI： 10.1109/ICDAR.2009.196

  • Capturing digital ink as retrieving fragments of document images Reviewed

    Kazumasa Iwata, Koichi Kise, Tomohiro Nakai, Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   1236 - 1240   2009.7

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    DOI： 10.1109/ICDAR.2009.192

  • Selecting and Evaluating Conspicuous Character Patterns Reviewed

    Seiichi Uchida, Ryoji Hattori, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the Third International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2009)   111 - 118   2009.7

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    Selecting and Evaluating Conspicuous Character Patterns

  • On a Possibility of Pen-Tip Camera for the Reconstruction of Handwritings Reviewed

    Seiichi Uchida, Katsuhiro Itou, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the Third International Workshop on Camera-Based Document Analysis and Recognition (CBDAR 2009)   119 - 126   2009.7

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    On a Possibility of Pen-Tip Camera for the Reconstruction of Handwritings

  • 自己発信情報の組み込みによる移動体の分離追跡

    〆野 敦稔, 内田 誠一, 倉爪 亮, 谷口 倫一郎, 長谷川 勉

    電気学会論文誌. C, 電子・情報・システム部門誌 = The transactions of the Institute of Electrical Engineers of Japan. C, A publication of Electronics, Information and System Society   129 ( 5 )   977 - 984   2009.5

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    Tracking of a moving robot in surveillance video is an important task for coexistence of human beings with robots. An essential technology to manage coexistence environment of human beings and moving robots is separation and tracking of moving robots. For this task, the moving robot should be separated from other moving objects, i.e., human beings. We assume that the robot provides its additional motion information to the surveillance system to ease the task. The robot can be tracked from the other objects as a moving region being consistent with the additional motion information. For this purpose, we modify a tracking algorithm based on particle filter in order to incorporate the additional motion information. The results of an experiment on real surveillance video sequences have indicated that the proposed framework can separate and track a moving robot under the existence of several walking persons.

    DOI： 10.1541/ieejeiss.129.977

  • Layout-free dewarplng of planar document images Reviewed

    Masakazu Iwamura, Ryo Niwa, Akira Horimatsu, Koichi Kise, Seiichi Uchida, Shinichiro Omachi

    Proceedings of SPIE - The International Society for Optical Engineering   7247   1 - 10   2009.1

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    DOI： 10.1117/12.806122

  • Identifying subscripts and superscripts in mathematical documents Reviewed

    Walaa Aly, Seiichi Uchida, Masakazu Suzuki

    Mathematics in Computer Science   2 ( 2 )   195 - 209   2008.12

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    DOI： 10.1007/s11786-008-0051-9

  • Early Recognition of Sequential Patterns by Classifier Combination Reviewed

    Seiichi Uchida, Kazuma Amamoto

    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6   3011 - 3014   2008.12

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    DOI： 10.1109/ICPR.2008.4761137

  • A New HMM for On-Line Character Recognition Using Pen-Direction and Pen-Coordinate Features Reviewed

    Yoshinori Katayama, Seiichi Uchida, Hiroaki Sakoe

    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6   781 - 784   2008.12

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    DOI： 10.1109/ICPR.2008.4761449

  • Fast Image Mosaicing Based on Histograms Reviewed

    Akihiro Mori, Seiichi Uchida

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E91D ( 11 )   2701 - 2708   2008.11

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    DOI： 10.1093/ietisy/e91-d.11.2701

  • データ埋め込みペンに関する基礎的検討

    田中 一弘, 内田 誠一, 岩村 雅一, 大町 真一郎, 黄瀬 浩一

    ヒューマンインタフェース学会論文誌   10 ( 4 )   559 - 567   2008.11

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  • Information Embedment with Cross Ratio of Areas for Accurate Camera-Based Character Recognition Reviewed

    Shinichiro Omachi, Masakazu Iwamura, Seiichi Uchida, Koichi Kise

    Proceedings of the Third Korea-Japan Joint Workshop on Pattern Recognition (KJPR 2008)   111 - 112   2008.11

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    Information Embedment with Cross Ratio of Areas for Accurate Camera-Based Character Recognition

  • A Large-Scale Analysis of Mathematical Expressions for an Accurate Understanding of Their Structure Reviewed

    Walaa Aly, Seiichi Uchida, Masakazu Suzuki

    PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS   549 - 556   2008.9

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    DOI： 10.1109/DAS.2008.53

  • Skew Estimation by Instances Reviewed

    Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS   201 - 208   2008.9

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    DOI： 10.1109/DAS.2008.22

  • Real-Time Nonlinear FEM with Neural Network for Simulating Soft Organ Model Deformation Reviewed

    Ken'ichi Morooka, Xin Chen, Ryo Kurazume, Seiichi Uchida, Kenji Hara, Yumi Iwashita, Makoto Hashizume

    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2008, PT II, PROCEEDINGS   5242 ( Pt 2 )   742 - 749   2008.9

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    DOI： 10.1007/978-3-540-85990-1_89

  • Affine Invariant Recognition of Characters by Progressive Pruning Reviewed

    Akira Horimatsu, Ryo Niwa, Masakazu Iwamura, Koichi Kise, Seiichi Uchida, Shinichiro Omachi

    PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS   237 - +   2008.9

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    DOI： 10.1109/DAS.2008.88

  • Feature Desynchronization in Online Character Recognition Reviewed International journal

    Seiichi Uchida, Kazuya Niyagawa, Hiroaki Sakoe

    Proceedings of the 11th International Conference on Frontiers of Handwriting Recognition   2008.8

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  • 座標特徴と方向特徴の選択的利用に基づくオンライン文字認識HMM(画像認識,コンピュータビジョン)

    片山 喜規, 内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D, 情報・システム   91 ( 8 )   2112 - 2120   2008.8

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    本論文では,高精度なオンライン文字認識のために,方向特徴並びに座標特徴を適切に使い分け可能な隠れマルコフモデル(HMM)を提案する.両特徴はいずれもオンライン文字認識の基本的な特徴量でありながら,全く異なった性質を示す.すなわち,線分内で方向特徴が定常的なのに対し,座標特徴は常に非定常である.したがって,HMMの枠組みにおいて両特徴を同等に扱うのは問題が多い.実際従来法では,座標特徴を用いずに方向特徴だけが用いられることが多かった.本論文で提案するHMMでは,方向特徴を状態内自己遷移における出力シンボルとして使用し,座標特徴を状態間遷移における出力シンボルとして使用する.このようにすることで,線分方向が一定した定常的な部分においては方向特微が,線分の方向が変化する過渡的な部分においては座標特徴が評価されることになる.このように特徴を使い分けることで,従来法に比べ認識精度を大幅に向上できることを,多画文字(漢字)の筆順フリー認識実験並びにその詳細な考察を通して示す.

  • Mathematical symbol recognition with support vector machines Reviewed

    Christopher Malon, Seiichi Uchida, Masakazu Suzuki

    PATTERN RECOGNITION LETTERS   29 ( 9 )   1326 - 1332   2008.7

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    DOI： 10.1016/j.patrec.2008.02.005

  • Verification of mathematical formulae based on a combination of context-free grammar and tree grammar Reviewed

    Akio Fujiyoshi, Masakazu Suzuki, Seiichi Uchida

    INTELLIGENT COMPUTER MATHEMATICS, PROCEEDINGS   5144   415 - +   2008.7

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    DOI： 10.1007/978-3-540-85110-3_35

  • 筆順変動を表現するHMMとそのオンライン文字認識への応用(画像認識,コンピュータビジョン)

    片山 喜規, 内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D, 情報・システム   91 ( 5 )   1434 - 1441   2008.5

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    本論文では,筆順フリーなオンライン文字認識の高精度化を目指し,(i)筆順変動の統計的モデルの構築,及び(ii)その認識における利用,の2点について検討する.一般に筆順フリー化には不自然な画対応の許容による誤認識の問題があるが,提案する筆順変動モデルを用いることでそれらを抑制できる.この筆順変動モデルは,筆順フリー認識のためのグラフモデル(キューブグラフ)の確率的拡張として定式化され,結果的に文字形状に関するゆう度と筆順のゆう度を同時に扱うことが可能な隠れマルコフモデル(HMM)の一種となる.公開されているオンライン文字データベース"HANDS-kuchibue.d-97-06-10"を用いた認識実験により,筆順変動モデル導入の有効性及び妥当性を明らかにした.

  • Fast 3D reconstruction of human shape and motion tracking by Parallel Fast Level Set Method Reviewed

    Yumi Iwashita, Ryo Kurazume, Kenji Hara, Seiichi Uchida, Ken'ichi Morooka, Tsutomu Hasegawa

    2008 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-9   980 - +   2008.5

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    DOI： 10.1109/ROBOT.2008.4543332

  • 逆投影と幾何拘束を用いた2D/3D位置合せ

    椛島 佑樹, 原 健二, 倉爪 亮, 岩下 友美, 諸岡 健一, 内田 誠一, 長谷川 勉

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   91 ( 5 )   1380 - 1392   2008.5

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    レンジセンサにより取得した幾何モデルにカラーセンサで撮影したテクスチャ画像を貼り付けて表示するテクスチャマッピングを容易に実現するには,テクスチャ画像と幾何モデルのみからカラー・レンジセンサ間の相対位置関係を知ることが望ましい.本論文では,幾何拘束に基づく大域的手法とエッジの対応付けに基づく局所的手法の組合せにより,センサ間の相対位置・姿勢を初期値の変動にロバストにかつ高精度に推定し,テクスチャ画像と幾何モデルの位置合せを実現する手法を提案する.本手法はまず,テクスチャ画像から稜線と平面領域を抽出する.次に,この稜線と平面領域を幾何モデルに逆投影し,対象における幾何拘束条件を推定しつつ,この拘束条件のもとでセンサ間の相対位置・姿勢の初期推定値を求める.最後に,テクスチャ画像と幾何モデルの各エッジ間の対応付けに基づき,センサ間の相対位置・姿勢を決定する.実験では,エッジ間の対応付けに基づく従来手法と比較して,位置合せの成功率が41%から75%に向上した.

  • Mosaicing-by-recognition for video-based text recognition Reviewed

    Seiichi Uchida, Hiromitsu Miyazaki, Hiroaki Sakoe

    PATTERN RECOGNITION   41 ( 4 )   1230 - 1240   2008.4

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    DOI： 10.1016/j.patcog.2007.08.005

  • Elastic matching techniques for handwritten character recognition Reviewed

    Seiichi Uchida

    Pattern Recognition Technologies and Applications: Recent Advances   17 - 38   2008.4

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    DOI： 10.4018/978-1-59904-807-9.ch002

  • Comparative study of path nomalizations for path prediction Reviewed International journal

    Yuji Shinomura, Tomotaka Harano, Toru Tamaki, Toshiyuki Amano, Kazufumi Kaneda, Seiichi Uchida

    Proceedings of 14th Korea-Japan Joint Workshop on Frontiers of Computer Vision   2008.1

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  • 事例に基づく文書画像の回転角推定(研究速報)

    内田 誠一, 酒井 恵, 岩村 雅一, 大町 真一郎, 黄瀬 浩一

    電子情報通信学会論文誌. D, 情報・システム   91 ( 1 )   136 - 138   2008.1

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    各文字の回転変形に対する変量と不変量を事例として学習しておき,それらを利用することで文書画像の回転角を推定する方法を提案する.本手法は,文字単位で回転角を効率的に推定するため,文字列が直線的かつ平行にレイアウトされているという仮定が不要であり,したがって様々なレイアウトの文書に利用可能である.

  • 実環境文字認識のための面積比による付加情報埋込(画像認識,コンピュータビジョン)

    大町 真一郎, 岩村 雅一, 内田 誠一, 黄瀬 浩一

    電子情報通信学会論文誌. D, 情報・システム   90 ( 12 )   3246 - 3256   2007.12

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    ディジタルカメラを入力デバイスとして実環境中の文字を高精度に認識するために,文字画像と同時に認識補助のための付加情報を提示する方法が検討されている.付加情報は,人間にとって自然な形で提示されること,及び,幾何学的変形に対してロバストに抽出できることが要求される.本論文では,これらの要求を満たす手法として,面積比を利用した付加情報提示手法を提案する.すなわち,文字パターンを2色で印字することを前提とし,それぞれの色の領域の面積比を特定の値とするようにデザインする.具体的には,文字に影を付加したり輪郭線を別の色とする.これらは文字パターンのデザインとして既に行われており,提案手法はその線幅や面積を変えるにすぎない.したがって,提案手法は様々な用途に広く応用することが可能である.面積比はアフィン変換に不変であり,アフィン変換を受けた環境においても誤りなく抽出されることが期待される.実際に付加情報を埋め込んだ文字パターンを作成し,ディジタルカメラで撮影された画像中の文字パターンから付加情報を抽出する実験を行い,提案手法の有効性を確認する.また,付加情報を用いて文字を認識する実験を行い,認識精度が向上することを確認する.

  • Logical DP matching for detecting similar subsequence Reviewed

    Uchida, Seiichi, Mori, Akihiro, Kurazume, Ryo, Taniguchi, Rin-ichiro, Hasegawa, Tsutomu

    COMPUTER VISION - ACCV 2007, PT I, PROCEEDINGS   4843   628 - +   2007.11

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    DOI： 10.1007/978-3-540-76386-4_59

  • Recognition of Engineering Drawing Entities: Review of Approaches. Reviewed

    Sergey Ablameyko, Seiichi Uchida

    Int. J. Image Graphics   7 ( 4 )   709 - 733   2007.10

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    Recognition of Engineering Drawing Entities: Review of Approaches.

    DOI： 10.1142/S0219467807002878

  • Color quantization for scene change detection Reviewed International journal

    Ryoji Hattori, Seiichi Uchida

    Proceedings of The First International Symposium on Information and Computer Elements   2007.9

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  • Predictive DP matching for on-line character recognition Reviewed

    Daiki Baba, Seiichi Uchida, Hiroaki Sakoe

    ICDAR 2007: NINTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS   674 - 678   2007.9

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    DOI： 10.1109/ICDAR.2007.4377000

  • Non-uniform slant correction for handwritten text line recognition Reviewed

    Roman Bertolami, Seiichi Uchida, Matthias Zimmermann, Horst Bunke

    ICDAR 2007: NINTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS   18 - +   2007.9

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    DOI： 10.1109/ICDAR.2007.4378668

  • Image pixel force fields and their application for color map vectorisation Reviewed

    V. Bucha, S. Uchida, S. Ablameyko

    ICDAR 2007: NINTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS   1228 - +   2007.9

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    DOI： 10.1109/ICDAR.2007.4377111

  • Extraction of embedded class information from universal character pattern Reviewed

    Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi

    ICDAR 2007: NINTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS   437 - +   2007.9

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    DOI： 10.1109/ICDAR.2007.4378747

  • Separation and tracking of moving object using rough motion information from the object Reviewed International journal

    Atsutoshi Shimeno, Seiichi Uchida, Ryo Kurazume, Rin-ichiro Taniguchi, Tsutomu Hasegawa

    Proceedings of The First International Symposium on Information and Computer Elements   2007.9

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  • Rectifying Perspective Distortion into Affine Distortion Using Variants and Invariants Reviewed

    Masakazu Iwamura, Ryo Niwa, Koichi Kise, Seiichi Uchida, Shinichiro Omachi

    Proceedings of the Second International Workshop on Camera-Based Document Analysis and Recognition 2007 (CBDAR 2007)   138 - 145   2007.9

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    Rectifying Perspective Distortion into Affine Distortion Using Variants and Invariants

  • Real time estimation of deforming organs by neural network for endoscopic surgery simulator Reviewed International journal

    Ken'ichi Morooka, Hiroshi Masuda, Ryo Kurazume, Xian Chen, Seiichi Uchida, Kenji Hara, Makoto Hashizume

    Proceedings of The First International Symposium on Information and Computer Elements   2007.9

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  • Instance-Based Skew Estimation of Document Images by a Combination of Variant and Invariant Reviewed

    Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the Second International Workshop on Camera-Based Document Analysis and Recognition 2007 (CBDAR 2007)   53 - 60   2007.9

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    Instance-Based Skew Estimation of Document Images by a Combination of Variant and Invariant

  • オートマトン制御付き最適セグメンテーション法とその実環境文字認識への応用(画像処理,<特集>画像の認識・理解論文)

    内田 誠一, 酒井 恵, 岩村 雅一, 大町 真一郎, 黄瀬 浩一

    電子情報通信学会論文誌. D, 情報・システム   90 ( 8 )   1966 - 1976   2007.8

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    本論文では,動的計画法(DP)と有限状態オートマトン(FSA)の組合せに基づいた,一次元信号の最適セグメンテーション手法を提案する.具体的には,信号の性質(例えば信号の値が高い区間と低い区間が交互に繰り返すと言った性質)をFSA表現した上で制約条件としてセグメンテーション問題に組み込み,その制約下での大局的最適セグメンテーションをDPにより効率的に求める.FSAの導入により,信号の性質と一致しないセグメンテーション結果は排除され,精度の向上が見込める.更に,FSA状態と各区間の対応結果によって各区間の意味付けも可能となる.本論文では本手法の詳細を述べるとともに,更にある種の実環境文字画像認識タスクに適用することでその有効性を評価する.

  • 論理判定型DPマッチングによる類似区間検出

    森 明慧, 内田 誠一, 倉爪 亮, 谷口 倫一郎, 長谷川 勉, 迫江 博昭

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   90 ( 8 )   2147 - 2156   2007.8

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    本論文では,論理判定型DPマッチングによる類似区間検出手法について提案する.論理判定型DPマッチングとは,サポートと呼ばれる論理関数を基準として用いて二つのパターン間の非線形マッチングを行うアルゴリズムである.本手法の特徴は,パターン間に複数存在する類似区間の始端及び終端をマッチングの過程で最適に決定していく点にある.また,本手法の有効性を評価するための一応用として,ジェスチャの基本動作抽出についても検討する.実験の結果,本手法の基本的な性能を示すことができた.

  • 解析的DPマッチング Reviewed

    内田誠一, 迫江博昭

    電子情報通信学会論文誌   675 - 679   2007.8

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  • 解析的DPマッチング(パターン認識と理解,<特集>画像の認識・理解論文)

    内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D, 情報・システム   90 ( 8 )   2137 - 2146   2007.8

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    パターン認識・画像処理において多用される弾性マッチング手法に動的計画法によるマッチング,いわゆるDPマッチングがある.DPマッチングは離散化された最適化問題の幅優先探索に基づく解法であり,したがって探索の幅が非常に大きくなる問題に対しては適用困難であった.この問題を解決すべく本論文では解析的DPマッチングを提案する.本手法では,マッチングの評価に用いられる局所的な誤差関数を二次関数近似することで,幅優先探索なしに解析的に近似解(二次関数近似された問題の厳密解)を与えることができる.本論文では一次元パターンに対するマッチングアルゴリズムを導出し,更に実際の問題に適用し得ることをオンライン文字データを用いて実験的に検証する.

  • 並列 Fast Level Set Method による移動体の高速な三次元形状復元

    岩下 友美, 倉爪 亮, 原 健二, 内田 誠一, 諸岡 健一, 長谷川 勉

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   90 ( 8 )   1888 - 1899   2007.8

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    多数台のカメラによりシーン内に存在する対象物体の全周の幾何情報及び光学情報を取得し,任意視点からの画像を生成する手法として,視体積交差法と多視点ステレオ法が提案されている.しかしこれらの手法は単一物体あるいはオクルージョンの生じない複数物体を対象とした手法であり,シーン内に複数物体が存在し物体間に相互オクルージョンが生じる場合,それぞれの物体形状を同時に復元することは困難であった.この問題に対し,我々はこれまでに高速な境界追跡手法であるFast Level Set Methodを複数ステレオ距離画像に適用し,複数対象物体の三次元形状をオクルージョンに頑強に復元するシステムを構築している.本論文では,これまでに構築したシステムを8台の計算機からなるPCクラスタへ実装し,Fast Level Set Method処理の並列計算により,より高速な三次元形状の復元を実現する.また対象物体が移動する場合,その移動方向を予測し,移動体を処理する計算機の計算負荷を低減することで,移動体の正確な三次元形状を遅れなく復元する手法を提案する.更に,舞踊の測定実験により,対象が高速に移動しても,従来システムと比較してより正確な三次元形状の復元が可能であることを示す.

  • Databases of mathematical documents Reviewed International journal

    Masakazu Suzuki, Christopher Malon, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   12 ( 1 )   302 - 306   2007.4

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  • Databases of mathematical documents Reviewed International journal

    Masakazu Suzuki, Christopher Malon, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   12 ( 1 )   302 - 306   2007.4

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  • Databases of mathematical documents Reviewed International journal

    Masakazu Suzuki, Christopher Malon, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   12 ( 1 )   302 - 306   2007.4

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  • Databases of mathematical documents Reviewed International journal

    Masakazu Suzuki, Christopher Malon, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   12 ( 1 )   302 - 306   2007.4

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  • Databases of mathematical documents Reviewed International journal

    Masakazu Suzuki, Christopher Malon, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   12 ( 1 )   302 - 306   2007.4

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  • Databases of mathematical documents Reviewed International journal

    Masakazu Suzuki, Christopher Malon, Seiichi Uchida

    Research Reports on Information Science and Electrical Engineering of Kyushu University   12 ( 1 )   302 - 306   2007.4

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  • 付加情報を用いるパターン認識(パターン認識)

    岩村 雅一, 内田 誠一, 大町 真一郎, 黄瀬 浩一

    電子情報通信学会論文誌. D, 情報・システム   90 ( 2 )   460 - 470   2007.2

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    本論文ではパターンが属するクラスの情報(付加情報)をパターンと同時に識別器に入力し,パターンと付加情報から矛盾のない答を導くことで誤認識を防ぐ方式を検討する.この方式では付加情報の情報量が増えれば増えるほど認識率は100%に近づく.そのため,従来のパターン認識のように,いかに認識性能を向上させるかではなく,ある認識率を達成するために必要な付加情報の情報量をいかに小さくできるかが課題となる.本論文では付加情報の割当方と認識性能の関係を導き,実験によりデモンストレーションする.

  • 付加情報を用いるパターン認識 Reviewed

    岩村雅一, 内田誠一, 大町真一郎, 黄瀬浩一

    電子情報通信学会論文誌   26 - 30   2007.1

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  • 実体を伴うプロアクティブヒューマンインタフェースのためのジェスチャの早期認識・予測に関する検討

    森 明慧, 内田 誠一, 倉爪 亮, 谷口 倫一郎, 長谷川 勉, 迫江 博昭

    日本ロボット学会誌 = Journal of Robotics Society of Japan   24 ( 8 )   954 - 963   2006.11

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    This paper concerns three topics for realizing embodied&ldquo;proactive&rdquo;human interface, where a humanoid is used as an interface capable of making some reaction against to user's gesture input in advance to the termination of the gesture. The first topic is early recognition of gestures: the recognition result of a gesture is provided at the beginning part of the gesture. The second topic is motion prediction: the subsequent posture of the person who makes a gesture is predicted by using the result of early recognition. The third topic is a network model constructed for improving the performance of early recognition and motion prediction. The effectiveness of these methods was shown by experimental results.

    DOI： 10.7210/jrsj.24.954

  • A Data-Embedding Pen Reviewed

    Seiichi Uchida, Kazuhiro Tanaka, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition (IWFHR-10)   2006.10

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    A Data-Embedding Pen

  • Construction of symbolic representation from human motion information Reviewed

    Yutaka Araki, Daisaku Arita, Rin-ichiro Taniguchi, Seiichi Uchida, Ryo Kurazume, Tsutomu Hasegawa

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS   4252   212 - 219   2006.10

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    DOI： 10.1007/11893004_27

  • ディジタルカメラによる文字・文書の認識・理解

    黄瀬 浩一, 大町 真一郎, 内田 誠一, 岩村 雅一

    電子情報通信学会誌   89 ( 9 )   836 - 841   2006.9

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    ディジタルスチルカメラやビデオカメラの普及と発展に伴って,撮影した画像内の文字・文書を情報処理に利用したいという要求が高まっている.本稿では,このようなカメラを用いた文字・文書の認識・理解を通して,我々は何を得ることができるのか,また実現には何が問題であり,現在どのような取組みがなされているのかについて解説する.加えて,残された研究課題について触れるとともに,エーザインタフェースへの適用の視点から筆者らが進めている新しい試みについても紹介する.

  • Affine invariant information embedment for accurate camera-based character recognition Reviewed

    Shinichiro Omachi, Seiichi Uchida, Masakazu Iwamura, Koichi Kise

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS   1098 - +   2006.8

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    DOI： 10.1109/ICPR.2006.229

  • Support vector machines for mathematical symbol recognition Reviewed

    Christopher Malon, Seiichi Uchida, Masakazu Suzuki

    STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, PROCEEDINGS   4109   136 - 144   2006.8

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    DOI： 10.1007/11815921_14

  • OCR fonts revisited for camera-based character recognition Reviewed

    Seiichi Uchida, Masakazu Wamura, Shinichiro Omachi, Koichi Kise

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS   1134 - +   2006.8

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    DOI： 10.1109/ICPR.2006.891

  • Interactive road extraction with pixel force fields Reviewed

    V. Bucha, S. Uchida, S. Ablameyko

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS   829 - +   2006.8

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    DOI： 10.1109/ICPR.2006.720

  • Gray-scale thinning by using a pseudo-distance map Reviewed

    A. Nedzved, S. Uchida, S. Ablameyko

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS   239 - +   2006.8

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    DOI： 10.1109/ICPR.2006.618

  • Embodied Proactive Human Interface "PICO-2" Reviewed

    Ryo Kurazume, Hiroaki Omasa, Seiichi Uchida, Rinichiro Taniguchi, Tsutornu Hasegawa

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS   1233 - +   2006.8

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    DOI： 10.1109/ICPR.2006.488

  • Early recognition and prediction of gestures Reviewed

    Akihiro Mori, Seiichi Uchida, Ryo Kurazume

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS   560 - +   2006.8

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    DOI： 10.1109/ICPR.2006.467

  • An efficient radical-based algorithm for stroke-order-free online Kanji character recognition Reviewed

    Wenjie Cai, Seiichi Uchida, Hiroaki Sakoe

    18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS   986 - +   2006.8

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    DOI： 10.1109/ICPR.2006.241

  • カメラによる文字認識のためのカテゴリー情報の埋込に関する検討(画像認識,コンピュータビジョン)

    内田 誠一, 岩村 雅一, 大町 真一郎, 黄瀬 浩一

    電子情報通信学会論文誌. D, 情報・システム   89 ( 2 )   344 - 352   2006.2

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    本研究は,バーコードと同程度の精度で三次元実環境中の文字パターンを認識することを目標としている.実環境中の文字パターンは,撮影状況により様々なひずみ,例えば射影変換ひずみを受ける.このため,通常の文字認識手法の延長線上でこの目標を達成しようとしても,相当の困難が予想される.そこで本論文では,文字そのものに機械可読性を補強するような情報を埋め込む方式を検討する.具体的には,文字画像に対し,しま模様状のパターンを埋め込む.このパターンを構成する各しまの幅から計算される複比は,文字パターンがどのように射影変換ひずみを受けてたとしても常に一定値となる.したがって,カテゴリーと複比の値をあらかじめ対応づけておけば,抽出された複比を識別の手掛りとして認識時に利用できる.シミュレーション実験の結果,複比と文字形状情報を併用することで,射影変換ひずみを受けても非常に高い認識精度が得られることが分かった.

  • Structural analysis of mathematical formulae with verification based on formula description grammar Reviewed

    S Toyota, S Uchida, M Suzuki

    DOCUMENT ANALYSIS SYSTEMS VII, PROCEEDINGS   3872   153 - 163   2006.2

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    DOI： 10.1007/11669487_14

  • カメラによる文字認識のためのカテゴリ情報の埋め込みに関する検討 Reviewed

    内田誠一, 岩村雅一, 大町真一郎, 黄瀬浩一

    電子情報通信学会論文誌   E87D ( 5 )   1247 - 1253   2006.2

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  • Design and recognition of human-readable and machine-readable patterns Reviewed

    Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    1st Joint Workshop on Machine Perception and Robotics (MPR2005)   2005.9

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    Design and recognition of human-readable and machine-readable patterns
    In this paper, design and recognition of human-readable and machine-readable patterns are investigated. Specifically speaking, we design character images printed with a horizontal stripe pattern, called a cross ratio pattern. The cross ratio derived from the cross ratio pattern represents the class information of the character. Since the cross ratio is invariant to projective distortion, the class information is extracted correctly regardless of camera angle. The character image itself is human-readable and therefore the character image with the cross ratio pattern is not only humanreadable and but also machine-readable and can be used as a medium for human-machine communication.

  • Quantitative analysis of mathematical documents Reviewed

    Seiichi Uchida, Akihiro Nomura, Masakazu Suzuki

    International Journal on Document Analysis and Recognition   7 ( 4 )   211 - 218   2005.9

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    DOI： 10.1007/s10032-005-0142-y

  • A survey of elastic matching techniques for handwritten character recognition Reviewed

    S Uchida, H Sakoe

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E88D ( 8 )   1781 - 1790   2005.8

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    DOI： 10.1093/ietisy/e88-d.8.1781

  • Mosaicing-by-recognition for recognizing texts captured in multiple video frames Reviewed International journal

    Seiichi Uchida, Hiromitsu Miyazaki, and Hiroaki Sakoe

    First International Workshop on Camera-Based Document Analysis and Recognition 2005 (CBDAR 2005, Seoul, Korea)   3 - 9   2005.8

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  • Recognition with Supplementary Information -How Many Bits Are Lacking for 100% Recognition?- Reviewed

    Masakazu Iwamura, Seiichi Uchida, Shinichiro Omachi, Koichi Kise

    Proceedings of the First International Workshop on Camera-Based Document Analysis and Recognition (CBDAR2005)   68 - 75   2005.8

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    Recognition with Supplementary Information -How Many Bits Are Lacking for 100% Recognition?-

  • Mosaicing-by-recognition for recognizing texts captured in multiple video frames Reviewed International journal

    Seiichi Uchida, Hiromitsu Miyazaki, and Hiroaki Sakoe

    First International Workshop on Camera-Based Document Analysis and Recognition 2005 (CBDAR 2005, Seoul, Korea)   3 - 9   2005.8

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  • Data Embedding for Camera-Based Character Recognition Reviewed

    Seiichi Uchida, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise

    Proceedings of the First International Workshop on Camera-Based Document Analysis and Recognition (CBDAR2005)   60 - 67   2005.8

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    Data Embedding for Camera-Based Character Recognition

  • Mosaicing-by-recognition for recognizing texts captured in multiple video frames Reviewed International journal

    Seiichi Uchida, Hiromitsu Miyazaki, and Hiroaki Sakoe

    First International Workshop on Camera-Based Document Analysis and Recognition 2005 (CBDAR 2005, Seoul, Korea)   3 - 9   2005.8

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  • Mosaicing-by-recognition for recognizing texts captured in multiple video frames Reviewed International journal

    Seiichi Uchida, Hiromitsu Miyazaki, and Hiroaki Sakoe

    First International Workshop on Camera-Based Document Analysis and Recognition 2005 (CBDAR 2005, Seoul, Korea)   3 - 9   2005.8

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  • Online character recognition based on elastic matching and quadratic discrimination Reviewed

    H Mitoma, S Uchida, H Sakoe

    EIGHTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, PROCEEDINGS   36 - 40   2005.8

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    DOI： 10.1109/ICDAR.2005.178

  • Mosaicing-by-recognition: a technique for video-based text recognition Reviewed

    H Miyazaki, S Uchida, H Sakoe

    EIGHTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, PROCEEDINGS   904 - 908   2005.8

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    DOI： 10.1109/ICDAR.2005.161

  • Mosaicing-by-recognition for recognizing texts captured in multiple video frames Reviewed International journal

    Seiichi Uchida, Hiromitsu Miyazaki, and Hiroaki Sakoe

    First International Workshop on Camera-Based Document Analysis and Recognition 2005 (CBDAR 2005, Seoul, Korea)   3 - 9   2005.8

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    Language：English  

  • Dewarping of document image by global optimization Reviewed

    H Ezaki, S Uchida, A Asano, H Sakoe

    EIGHTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, PROCEEDINGS   302 - 306   2005.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2005.87

  • An HMM implementation for on-line handwriting recognition based on pen-coordinate feature and pen-direction feature Reviewed

    D Okumura, S Uchida, H Sakoe

    Eighth International Conference on Document Analysis and Recognition, Vols 1 and 2, Proceedings   26 - 30   2005.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2005.50

  • A ground-truthed mathematical character and symbol image database Reviewed

    M Suzuki, S Uchida, A Nomura

    Eighth International Conference on Document Analysis and Recognition, Vols 1 and 2, Proceedings   675 - 679   2005.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2005.14

  • Mosaicing-by-recognition for recognizing texts captured in multiple video frames Reviewed International journal

    Seiichi Uchida, Hiromitsu Miyazaki, and Hiroaki Sakoe

    First International Workshop on Camera-Based Document Analysis and Recognition 2005 (CBDAR 2005, Seoul, Korea)   3 - 9   2005.8

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    Language：English  

  • 情報付加による認識率100%の実現 －人にも機械にも理解可能な情報伝達のために－

    岩村雅一, 内田誠一, 大町真一郎, 黄瀬浩一

    画像の認識・理解シンポジウム2005講演論文集   901 - 908   2005.7

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    Language：Japanese  

  • 部首単位標準パターンとキューブサーチに基づく筆順フリーなオンライン文字認識アルゴリズム(画像認識, コンピュータビジョン)

    蔡 文杰, 内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-II, 情報・システム, II-パターン処理   88 ( 7 )   1187 - 1195   2005.7

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    Language：Japanese  

    グラフサーチにより最適画間対応を定めて筆順自由性を実現するオンライン文字認識法であるキューブサーチ法の動作速度と認識精度の改善を検討した.筆順変動を部首内の変動と部首間の変動に分離して, 部首単位標準パターンに基づく2段階のキューブサーチアルゴリズムを構成した.併せて, 処理量最小化条件を含む部首単位分割の指針を示した.教育漢字を対象とする画数固定条件での認識実験により, 速度, 精度両面での改善が確認され, 併せて, 処理量最小化部首分割条件の妥当性が確認された.

  • 部首単位標準パターンとキューブサーチに基づく筆順フリーなオンライン文字認識アルゴリズム Reviewed

    蔡 文杰, 内田誠一, 迫江博昭

    電子情報通信学会論文誌(D-II)   36 ( 9 )   2031 - 2040   2005.6

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    Language：Japanese  

  • Category-dependent elastic matching based on a linear combination of eigen-deformations Reviewed

    Seiichi Uchida, Hiroaki Sakoe

    Systems and Computers in Japan   36 ( 5 )   13 - 22   2005.5

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1002/scj.20229

  • ステレオ画像圧縮のための視差補償法に関する検討 Reviewed

    原 学, 内田誠一, 迫江博昭

    九州大学大学院システム情報科学紀要   572 - 575   2005.3

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    Language：Japanese  

  • Foreword: Special section on document image understanding and digital documents

    Kazuhiko Yamamoto, Shinji Tsuraoka, Hiromichi Fujisawa, Toyohide Watanabe, Hiroshi Murase, Yoshimasa Kimura, Fumitaka Kimura, Masaki Nakagawa, Ryuichi Oka, Norihiro Hagita, Satoshi Naoi, Yasuto Ishitani, Keiji Yamada, Daisuke Nishiwaki, Yoshihiko Hamamoto, Toru Wakahara, Koichi Kise, Shin'ichiro Omachi, Seiichi Uchida

    IEICE Transactions on Electronics   E88-C   1779 - 1780   2005.1

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    Language：Others  

    Foreword: Special section on document image understanding and digital documents

  • 弾性マッチングと固有変形を用いたオンライン文字認識(画像情報)(<特集>次世代移動体通信システム)

    三苫 寛人, 内田 誠一, 迫江 博昭

    情報処理学会論文誌   45 ( 12 )   2845 - 2855   2004.12

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    Language：Japanese  

    DPマッチングなど,いわゆる弾性マッチングに基づくオンライン文字認識においては,合わせ過ぎによる誤認識が発生する.たとえば,入力パターンが"1"であっても,マッチングによって水平部が非線形伸縮しながら対応付けられた結果,"7"に誤認識される場合がある.本論文では,こうした誤認識の低減手法を提案する.合わせ過ぎの発生原因としては,弾性マッチングが本来そのカテゴリでは起こりえないような変形も補償対象としていることがあげられる.そこで本手法では,あらかじめ各カテゴリに生じやすい変形(固有変形)を統計的手法により求めておき,認識の際のマッチングの結果が固有変形からどれぐらい逸脱しているかを評価する.その逸脱量が大きければ,そのマッチングにより合わせ過ぎが起きていると判断できる.オンライン数字データを用いた認識実験により,本手法の有効性を確認した.

  • 粗密DPによる画像の弾性マッチングの高速化 Reviewed

    宮崎洋光, 内田誠一, 迫江博昭

    九州大学大学院システム情報科学紀要   434 - 438   2004.9

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    Language：Japanese  

  • Online character recognition using eigen-deformations Reviewed

    R Mitoma, S Uchida, H Sakoe

    NINTH INTERNATIONAL WORKSHOP ON FRONTIERS IN HANDWRITING RECOGNITION, PROCEEDINGS   3 - 8   2004.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/IWFHR.2004.79

  • Prototype setting for elastic matching-based image pattern recognition Reviewed

    N Matsumoto, S Uchida, H Sakoe

    PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1   224 - 227   2004.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICPR.2004.1334064

  • 弾性マッチングに基づく画像パターン認識のための標準パターン設定法に関する検討

    松本 直樹, 内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-II, 情報・システム, II-パターン処理   87 ( 7 )   1539 - 1542   2004.7

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    Language：Japanese  

    弾性マッチングに基づく画像パターン認識のための標準パターン設定法について述べる.本手法はクラスタリング法の一種であるが,従来法がユークリッド距離を基準としているのに対し,本手法では識別時と同じ弾性マッチングによる距離を基準とする.

  • Human action sensing for proactive human interface: Computer vision approach Reviewed International journal

    R. Taniguchi, D. Arita, S. Uchida, R. Kurazume, and T. Hasegawa

    Proceedings of International workshop on Processing Sensory Information for Proactive Systems (PSIPS 2004, Oulu, Finland)   2004.6

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  • Nonuniform slant correction for handwritten word recognition Reviewed

    E Taira, S Uchida, H Sakoe

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E87D ( 5 )   1247 - 1253   2004.5

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    Language：English   Publishing type：Research paper (scientific journal)  

  • Block boundary detection and title extraction for automatic bookshelf inspection Reviewed International journal

    Eiji Taira, Seiichi Uchida, and Hiroaki Sakoe

    Tenth Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2005, Fukuoka, Japan)   2004.2

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  • モデル当てはめによる書棚画像解析(画像処理,画像パターン認識)

    平 英二, 高山 誠悟, 内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-II, 情報・システム, II-パターン処理   87 ( 2 )   565 - 573   2004.2

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    本論文では画像処理による書籍管理を目的として書棚画像から各書籍の境界を検出する手法を提案する.従来法ではエッジや影からハフ変換などの直線検出法を用いて書籍境界を検出している.本手法では,そのような局所的な情報だけでなく大域的な最適性も考慮して,書棚画像の最適領域分割(各書籍の背表紙領域,書棚背景領域)を動的計画法に基づくアルゴリズムにより行い,各書籍の境界を検出する.更に最適化問題として定式化する際,書棚画像の文法モデルを組み込むことで高精度化を図っている.実験により,本手法の有効性を定性的及び定量的に確認した.

  • カテゴリー固有変形の線形結合モデルに基づく弾性マッチング法(画像処理,画像パターン認識)

    内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-II, 情報・システム, II-パターン処理   87 ( 2 )   639 - 648   2004.2

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    Language：Japanese  

    画像パターンの認識において,パターンに生じた変形を補償するための手法として,弾性マッチングの利用が検討されている.従来法がすべてのカテゴリーに共通の変形特性を仮定していたのに対し,本論文では各カテゴリーに固有の変形特性を組み込んだ手法を提案する.具体的には,各カテゴリーの任意の変形をそのカテゴリーに固有ないくつかの変形の線形結合で表現する.その結果,各カテゴリー内に生じる変形だけが適切に補償されることになり,過変形の抑制及び計算効率の向上といった効果が得られる.本手法は,一種の非線形最適化問題として定式化される.本論文ではその解法についても述べ,実験を通して有効性を検証する.

  • A model-based book boundary detection technique for bookshelf image analysis Reviewed International journal

    Eiji Taira, Seiichi Uchida, and Hiroaki Sakoe

    Asian Conference on Computer Vision (ACCV2004, Jeju Island, Korea)   2004.1

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    Language：English  

  • A comparative study of stroke correspondence search algorithms for online kanji character recognition International journal

    Wenjie Cai, Seiichi Uchida, and Hiroaki Sakoe

    International Symposium on Information Science and Electrical Engineering   E83D ( 1 )   109 - 111   2003.11

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    Language：English  

  • INFTY: an integrated OCR system for mathematical documents. Reviewed

    Masakazu Suzuki, Fumikazu Tamari, Ryoji Fukuda, Seiichi Uchida, Toshihiro Kanahori

    Proceedings of the 2003 ACM Symposium on Document Engineering, Grenoble, France, November 20-22, 2003   95 - 104   2003.11

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    INFTY: an integrated OCR system for mathematical documents.

  • Book boundary detection from bookshelf image based on model fitting International journal

    Eiji Taira, Seiichi Uchida, and Hiroaki Sakoe

    International Symposium on Information Science and Electrical Engineering   534 - 537   2003.11

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  • Eigen-deformations for elastic matching based handwritten character recognition Reviewed

    S Uchida, H Sakoe

    PATTERN RECOGNITION   36 ( 9 )   2031 - 2040   2003.9

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/S0031-3203(03)00039-6

  • Detection and segmentation of touching characters in mathematical expressions Reviewed

    A Nomura, K Michishita, S Uchida, M Suzuki

    SEVENTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS   126 - 130   2003.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2003.1227645

  • Handwritten character recognition using elastic matching based on a class-dependent deformation model Reviewed

    S Uchida, H Sakoe

    SEVENTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS I AND II, PROCEEDINGS   163 - 167   2003.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2003.1227652

  • A handwritten character recognition method based on unconstrained elastic matching and eigen-deformations Reviewed

    S Uchida, H Sakoe

    EIGHTH INTERNATIONAL WORKSHOP ON FRONTIERS IN HANDWRITING RECOGNITION: PROCEEDINGS   72 - 77   2002.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/IWFHR.2002.1030887

  • Using eigen-deformations in handwritten character recognition Reviewed

    S Uchida, MA Ronee, H Sakoe

    16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL I, PROCEEDINGS   572 - 575   2002.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICPR.2002.1044795

  • An efficient correlation computation method for binary images based on matrix factorisation Reviewed

    R Bogush, S Maltsev, S Ablameyko, S Uchida, S Kamata

    SIXTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, PROCEEDINGS   312 - 316   2001.9

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2001.953805

  • Nonuniform slant correction using dynamic programming Reviewed

    S Uchida, E Taira, H Sakoe

    SIXTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, PROCEEDINGS   434 - 438   2001.9

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2001.953827

  • Handwritten character recognition using piecewise linear two-dimensional warping Reviewed

    MA Ronee, S Uchida, H Sakoe

    SIXTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, PROCEEDINGS   39 - 43   2001.9

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.2001.953751

  • 区分線形2次元ワープ法の検討

    内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-II, 情報・システム, II-パターン処理   83 ( 12 )   2622 - 2629   2000.12

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    2画像の最大一致を与える2次元-2次元写像を2次元ワープと呼ぶ.パターン認識の立場から見れば, 2次元ワープは画像の弾性マッチング処理であり, 同時に画素をプリミティブとする構造解析処理でもある.筆者らは単調連続性の条件下で純粋に非線形な2次元ワープを探索する動的計画アルゴリズムを検討してきたが, 計算量が画像サイズの指数オーダとなる問題があった.その改善を目指し, 本論文では区分線形2次元ワープ法を提案する, 本手法では画像の各行のワープによる像は折れ線となる.ワープの最適化はこの折れ線の屈曲点の位置に関して行われる.最適ワープを求めるための計算量は単調連続2次元ワープの場合に比べて大幅に低減される.計算機実験を通じて, 本手法の有効性及び問題点を考察する.

  • Piecewise linear two-dimensional warping Reviewed

    S Uchida, H Sakoe

    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS   534 - 537   2000.9

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICPR.2000.903601

  • 単調連続2次元ワープ法によるオフライン手書き文字認識実験

    内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-II, 情報・システム, II-パターン処理   83 ( 4 )   1198 - 1200   2000.4

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  • An approximation algorithm for two-dimensional warping

    S Uchida, H Sakoe

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E83D ( 1 )   109 - 111   2000.1

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  • Handwritten character recognition using monotonic and continuous two-dimensional warping Reviewed

    Seiichi Uchida, Hiroaki Sakoe

    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR   503 - 506   1999.9

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICDAR.1999.791834

  • An efficient two-dimensional warping algorithm Reviewed

    S Uchida, H Sakoe

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E82D ( 3 )   693 - 700   1999.3

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    Language：English   Publishing type：Research paper (scientific journal)  

  • A monotonic and continuous two-dimensional warping based on dynamic programming Reviewed

    S Uchida, H Sakoe

    FOURTEENTH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1 AND 2   521 - 524   1998.8

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    Language：English   Publishing type：Research paper (other academic)  

    DOI： 10.1109/ICPR.1998.711195

  • 動的計画法に基づく単調連続2次元ワープ法の検討

    内田 誠一, 迫江 博昭

    電子情報通信学会論文誌. D-2, 情報・システム 2-情報処理   81 ( 6 )   1251 - 1258   1998.6

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    Language：Japanese  

    2画像間の最大一致を実現する画素間のマッピングとして定義される2次元ワープは, パターンに生じる変形に適応可能なテンプレートマッチング法とみなすことができる.本論文では新しい2次元ワープ法の枠組みを提案し, 基礎的な考察を行う.本手法の第一の特徴は, 2次元的な自由度をもちながら, パターンの位相を保存するワープを構成できることである.この性質はワープに対する単調性および連続性制約により実現される.第2の特徴は, 画像全体での最適性が保証されるように構成された動的計画法(DP)を, 最大一致の探索法として用いる点である.DPの利用により, 評価関数に対する微分可能性の制約がないなどの特長も生じる.実験により, 提案した手法の基本的特性を確認した.

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  • 教養としてのデータサイエンス (データサイエンス入門シリーズ)

    北川 源四郎, 竹村 彰通 (編集), 内田 誠一, 川崎 能典, 孝忠 大輔, 佐久間 淳, 椎名 洋, 中川 裕志, 樋口 知之, 丸山 宏（著）（ Role： Joint author）

    講談社  2021.6 

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    Language：Japanese   Book type：Scholarly book

  • 巻頭言 「オープンマインド溢れるオープンナレッジ」ンピュータビジョン最前線 Spring 2023）

    内田誠一

    共立出版  2023.3 

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    Responsible for pages：総ページ数：149p   Language：Japanese  

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    内田誠一（ Role： Contributor）

    共立出版  2023.3    （ ISBN:9784320125476 ）

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  • 応用基礎としてのデータサイエンス AI×データ活用の実践 (データサイエンス入門シリーズ)

    赤穂 昭太郎, 今泉 允聡, 内田 誠一, 清 智也, 高野 渉, 辻 真吾, 原 尚幸, 久野 遼平, 松原 仁, 宮地 充子, 森畑 明昌, 宿久 洋, 森畑, 明昌, 宿久, 洋（ Role： Contributor）

    講談社  2023.2    （ ISBN:4065307899 ）

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  • 応用基礎としてのデータサイエンス AI×データ活用の実践 (データサイエンス入門シリーズ)

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    講談社  2023.2 

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    Responsible for pages：総ページ数：384   Language：Japanese  

  • 応用基礎としてのデータサイエンス : AI×データ活用の実践

    北川 源四郎 , 竹村 彰通 , 赤穂 昭太郎, 今泉 允聡 , 内田 誠一, 清 智也, 高野 渉, 辻 真吾 , 原 尚幸, 久野 遼平 , 松原 仁 , 宮地 充子 , 森畑 明昌, 宿久 洋

    講談社  2023    （ ISBN:9784065307892 ）

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  • 広島ゆかりの文学

    安田女子大学文学部日本文学科内日本文学科ブランディング委員会, 古瀬 雅義 , キューン ミッシェル, 島田 大助, 内田 誠一, 外村 彰

    安田女子大学文学部日本文学科内日本文学科ブランディング委員会  2023    （ ISBN:9784902782134 ）

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    Verla Uchida Adrianne, Roloff Rothman Jennie, Farrell Thomas S. C. (Thomas Sylvester Charles)

    Candlin & Mynard ePublishing  2023    （ ISBN:9798861658867 ）

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  • 機械学習のさまざまな問題設定と解法(第3章-I-3)

    備瀬 竜馬, 内田 誠一（ Role： Joint author）

    羊土社  2020.12 

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    Responsible for pages：機械学習を生命科学に使う！ シークエンスや画像データをどう解析し、新たな生物学的発見につなげるか？ (小林徹也，杉村 薫，舟橋 啓 編) 実験医学増刊, vol.38, no.20,   Language：Japanese   Book type：Scholarly book

  • 医用画像全般に使えるパターン認識，機械学習(第1章)

    内田 誠一（ Role： Joint author）

    オーム社  2020.4 

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    Responsible for pages：放射線治療AIと外科治療AI（医療AIとディープラーニングシリーズ）   Language：Japanese   Book type：Scholarly book

  • Reading-Life Log as a New Paradigm of Utilizing Character and Document Media

    Koichi Kise, Shinichiro Omachi, Seiichi Uchida, Masakazu Iwamura, Masahiko Inami, Kai Kunze（ Role： Joint author）

    Springer Japan  2017.4 

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    Language：English   Book type：Scholarly book

  • 画像データの基礎知識～画像の種類と見せ方～(第1章-4) トラッキングの基礎とその周辺(第3章-2)

    （ Role： Joint author）

    羊土社  2014.11 

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    Language：Japanese   Book type：Scholarly book

  • Text Localization and Recognition in Images and Video

    Seiichi Uchida（ Role： Joint author）

    Springer-Verlag  2014.1 

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    Language：English   Book type：Scholarly book

  • Data-Embedding Pen

    Seiichi Uchida, Marcus Liwicki, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise（ Role： Joint author）

    IGI Global  2012.10 

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    Language：English   Book type：Scholarly book

  • Statistical Deformation Model for Handwritten Character Recognition (in Recent Advances in Document Recognition and Understanding)

    Seiichi Uchida（ Role： Joint author）

    InTech  2011.10 

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    Language：English   Book type：Scholarly book

  • 中世英文学資料のデジタル化の試み(Anglo-Saxon語の継承と変容II 中世英文学)

    山口晃典, 内田誠一, 千葉淳一, 飯田周作, 植竹朋文, 松下知紀（ Role： Joint author）

    専修大学出版局  2010.2 

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  • 文字認識による中世英文学資料のデジタル化の試み(ことばの普遍と変容 (Anglo-Saxon語の継承と変容 叢書4))

    山口晃典, 内田誠一, 千葉淳一, 飯田周作, 植竹朋文, 松下知紀（ Role： Joint author）

    専修大学社会知性開発研究センター  2009.4 

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  • Elastic matching in handwritten character recognition (in Pattern Recognition Technologies and Applications: Recent Advances)

    Seiichi Uchida（ Role： Joint author）

    IGI Global  2008.6 

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Presentations

  • Conditional GANによる医療画像のデータ拡張

    竹崎 隼平, 内田 誠一, 田中 聖人, 門田 健明

    電気関係学会九州支部連合大会講演論文集  2022.9 

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    Event date： 2022.9

    Language：Japanese  

    Country：Other  

    本研究では，アノテーション付きのデータを生成可能であるConditional GAN (cGAN)を使用したデータ拡張を行うことで，医療画像識別の精度改善を図る．現在，深層学習を用いた医療画像解析では，アノテーション付きの医療画像のデータ不足が深刻な問題となっている．従来のデータ拡張と比較して，cGANは学習データに存在しないデータを生成できる点で優れている．したがって，従来より多様なデータをモデルに学習させることが可能となり，識別精度の改善に繋がると考えられる．我々は，内視鏡画像を用いた実験を行い，cGANによるデータ拡張の有用性，及び従来のデータ拡張と比較した場合の優位性について考察する．

  • 適応的データバランス調整～オンライン予測の理論に基づくアプローチ～

    斉藤 優也, 内田 誠一, 末廣 大貴

    電気関係学会九州支部連合大会講演論文集  2022.9 

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    Event date： 2022.9

    Language：Japanese  

    Country：Other  

    本研究では，オンライン予測の考えに基づく重み付けアルゴリズムをクラスインバランス問題に対して適用する手法を提案する．多クラス分類問題において，クラス間のサンプル数が不均衡であるデータ（インバランスデータ）では，各クラスの損失に対して適切な重みを与えて学習をさせる必要がある．しかし，予め各クラスに対して最適な重みを決定するのは非常に困難である．そこで本研究では，オンライン予測の考えに基づき逐次的な重み付けを行う手法を提案する．この重み付けは，学習毎の損失の結果に基づき行われるため，学習器の挙動に応じて適応的に重み付けが可能となる．

  • 文字画像における敵対的サンプルの生成

    片岡 蓮太郎, 内田 誠一

    電気関係学会九州支部連合大会講演論文集  2022.9 

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    Event date： 2022.9

    Language：Japanese  

    Country：Other  

    本研究では，文字画像に対して人間には認識できないレベルの微小なノイズを与えることにより，敵対的サンプルを生成することを目的とする．文字画像は一般画像とは異なり，白と黒の２値で表される．そのため，既存の手法でノイズを与えた際に画像の変化が顕著となり，不自然な画像が生成される．そこで本研究では，損失関数の勾配の絶対値が大きい画素にのみノイズを与えて，白または黒とする手法と，文字を表す画素周辺の画素にのみ画素値の変化を与える手法を提案する．提案手法を用いることで，文字らしさを維持しつつ機械学習モデルに誤分類をさせる文字画像を生成することが可能になると考えられる．

  • Transformer によるデータ拡張手法の適応的選択

    山田 敏輝, 原田 翔太, 内田 誠一

    電気関係学会九州支部連合大会講演論文集  2022.9 

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    Event date： 2022.9

    Language：Japanese  

    Country：Other  

    本研究の目的は，Transformerによるデータ拡張手法の適応的選択である．データ拡張とは，手持ちのデータに加工を加えて，学習データ数を水増しする方法であり，タスクやデータの特性により有効な加工法が異なるという課題があった．そこで本研究では，異なる複数の方法で加工したデータを入力としてTransformerを学習することで，Transformerの内部で適切な加工法を自動的に取捨選択させる方法を提案する．さらに，学習済みのTransformerを解析することで，どの加工法が有効であったかを解明する．

  • Neural Style Difference Transferを用いたフォント生成

    近藤 徹多, Atarsaikhan Gantugs, 内田 誠一

    電気関係学会九州支部連合大会講演論文集  2022.9 

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    Event date： 2022.9

    Language：Japanese  

    Country：Other  

    本研究では，NSDT (Neural Style Difference Transfer) を用いたフォント生成手法を提案する．同手法の原型であるNSTでは，ニューラルネットワークを用いてある画像のスタイルを別画像に転用する手法である．これに対しNSDTでは，2フォント間のスタイル差異を別のフォントに転用する．本発表では，このNSDTで生成されるフォントの可読性を向上すべく，新たに識別可能性を考慮した損失関数の導入を試みる．そしてフォント生成実験を通して向上効果を検証する．

  • Energy-Based Modelに基づく識別器の信頼度較正

    鳥羽 真仁, 内田 誠一, 早志 英朗

    電気関係学会九州支部連合大会講演論文集  2022.9 

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    Event date： 2022.9

    Language：Japanese  

    Country：Other  

    本研究ではEnergy-Based Model (EBM) に基づく識別器の信頼度較正手法を提案する．提案法では，Neural Network (NN) を識別器として学習させる際，生成モデルであるEBMと特徴抽出部を共有して学習させる．これによりクラス事後確率だけでなく，入力データ分布も同時に学習させることで信頼度較正が期待できる．実験では医用データセットであるMedMNISTを用いて学習を行い，NNが出力する信頼度がEBMにより適切に較正されたかをExpected Calibration Errorにより評価する．

  • Data Augmentation of Medical Images Based on Conditional GAN

    Record of Joint Conference of Electrical and Electronics Engineers in Kyushu  2022.9  Committee of Joint Conference of Electrical, Electronics and Information Engineers in Kyushu

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    Event date： 2022.9

    Language：Japanese  

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  • Generating of adversarial examples in character images

    Record of Joint Conference of Electrical and Electronics Engineers in Kyushu  2022.9  Committee of Joint Conference of Electrical, Electronics and Information Engineers in Kyushu

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    Event date： 2022.9

    Language：Japanese  

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  • Adaptive Selection of Data Augmentation Methods

    Record of Joint Conference of Electrical and Electronics Engineers in Kyushu  2022.9  Committee of Joint Conference of Electrical, Electronics and Information Engineers in Kyushu

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    Event date： 2022.9

    Language：Japanese  

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  • Font Style Transfer

    Atarsaikhan Gantugs

    Record of Joint Conference of Electrical and Electronics Engineers in Kyushu  2022.9  Committee of Joint Conference of Electrical, Electronics and Information Engineers in Kyushu

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    Event date： 2022.9

    Language：Japanese  

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  • Image classification

    Record of Joint Conference of Electrical and Electronics Engineers in Kyushu  2022.9  Committee of Joint Conference of Electrical, Electronics and Information Engineers in Kyushu

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    Event date： 2022.9

    Language：Japanese  

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  • クラス情報を考慮したEnergy-based Modelによる時系列予測手法の提案

    山縣将貴, 内田誠一, 早志英朗

    情報処理学会研究報告(Web)  2022 

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    Event date： 2022

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  • A model study for genome-wide cis-decoding with explainable deep learning in kiwifruit ripening responses

    桑田恵理子, 竹下孔喜, 藤田尚子, 内田誠一, 赤木剛士, 赤木剛士

    日本植物生理学会年会(Web)  2022 

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    Event date： 2022

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  • Invariant Information Clusteringの挙動解析

    北島和樹, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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    Event date： 2021

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  • Prediction of enriched small RNA sequences with deep learning for application to horticultural crops

    榎那津美, 増田佳苗, 久保康隆, 牛島幸一郎, 内田誠一, 赤木剛士, 赤木剛士

    園芸学研究 別冊  2021 

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  • Genome-wide cis-decoding for expression designing in tomato using explainable deep learning

    赤木剛士, 赤木剛士, 増田佳苗, 桑田恵理子, 竹下孔喜, 川勝泰二, 有泉亨, 久保康隆, 牛島幸一郎, 内田誠一

    園芸学研究 別冊  2021 

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  • Genome-wide survey of cis-motifs responsible for fruit ripening in kiwifruit, with explainable deep learning

    桑田恵理子, 竹下孔喜, 藤田尚子, 牛島幸一郎, 久保康隆, 別府賢治, 片岡郁雄, 内田誠一, 赤木剛士, 赤木剛士

    園芸学研究 別冊  2021 

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  • Prediction and premonitory symptoms characterization for rapid over-softening in persimmon fruit, with deep learning

    鈴木茉莉亜, 増田佳苗, 竹下孔喜, 朝隈英昭, 杉浦真由, 鈴木哲也, 新川猛, 久保康隆, 牛島幸一郎, 内田誠一, 赤木剛士, 赤木剛士

    園芸学研究 別冊  2021 

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  • 深層ニューラルネットワークを用いたSharp Score評価領域の自動検出及び正常か異常かの自動判定

    美山和毅, 美山和毅, 備瀬竜馬, 池村聡, 甲斐一広, 中島康晴, 内田誠一

    九州リウマチ学会プログラム抄録集  2021 

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  • 情景内文字とキャプションの相関解析

    中村亘岐, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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  • 単語の意味と画像の共潜在空間埋め込み

    安河内直哉, 松尾信之介, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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  • Logo Images Classification

    石為之, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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    Event date： 2021

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  • Vision Transformerを用いた数字画像の欠損補間

    中鶴慧, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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    Event date： 2021

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  • Vision Transformerを用いた多フォント文字認識

    大峠仁輝, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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    Event date： 2021

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  • Transformerを用いたアウトライン文字認識

    永田悠祐, 内田誠一

    電気・情報関係学会九州支部連合大会講演論文集(CD-ROM)  2021 

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  • 低解像度画像からの情景内文字検出手法

    塩山惇太郎, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese  

    Venue：大分大学   Country：Japan  

  • オンライン人流予測

    ソン ホン, 末廣大貴, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：English  

    Venue：大分大学   Country：Japan  

  • オンラインエキスパート統合アルゴリズムに基づく異常検知

    満尾成亮, 末廣大貴, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese  

    Venue：大分大学   Country：Japan  

  • CNN層による異常検出

    ジ ショウトン, Yuchen Zheng, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：English  

    Venue：大分大学   Country：Japan  

  • CNNを用いたアンサンブル学習による画像分類

    杉原麻美子, Yuchen Zheng, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese  

    Venue：大分大学   Country：Japan  

  • 混合正規分布に基づくニューラルネットワークのスパースベイズ学習

    早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：福岡工業大学   Country：Japan  

  • 時系列データのための掛け算レイヤ

    李　俊鎬, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：English   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • CNNを用いたアンサンブル学習による画像分類

    杉原麻美子, Yuchen Zheng, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • 低解像度画像からの情景内文字検出手法

    塩山惇太郎, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • 畳み込みオートエンコーダによる花押画像解析

    鬼塚洋輔, 大山 航, 山田太造, 井上 聡, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • 画像に基づく言語翻訳

    馬場康平, Iwana Brian, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • 情景内の文字情報と画像キャプションの類似性解析

    竹下孔喜, 生駒真也, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • オンラインエキスパート統合アルゴリズムに基づく異常検知

    満尾成亮, 末廣大貴, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • 欠損ありデータを用いた変光星の分類

    長谷川雄大, 板 由房, 田中雅臣, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • CNN層による異常検出

    ジ ショウトン, Yuchen Zheng, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：English   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • オンライン人流予測

    ソン ホン, 末廣大貴, 内田誠一

    電気・情報関係学会九州支部連合大会  2018.9 

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    Language：English   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • オープンサイエンス＆オープンエデュケーション with オープンマインド Invited

    内田誠一

    愛媛大学数学談話会  2018.10 

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    Language：Japanese  

    Venue：愛媛大学   Country：Japan  

  • オープンサイエンス＆オープンエデュケーション with オープンマインド Invited

    内田誠一

    愛媛大学数学談話会  2018.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：愛媛大学   Country：Japan  

  • 内視鏡画像の臓器等への自動分類 Invited

    内田誠一

    消化器内視鏡領域におけるAI研究実績報告会  2018.11 

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    Language：Japanese  

    Venue：神戸商工会議所 神商ホール   Country：Japan  

  • グラフカットとCNNを用いたマウス胚領域分割

    原田大輔, 備瀬竜馬, 岡 早苗, Timothy Francis Day, 藤森俊彦, 内田誠一

    電子情報通信学会医用画像研究会  2018.11 

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    Language：Japanese  

    Venue：兵庫県立大学 神戸情報科学キャンパス   Country：Japan  

  • Biomedical image analysis as an interesting machine learning task Invited

    Shonan Meeting No.128 Workshop on Patient Similitude: Combining Histopathological Images & Multiple-Scale Molecular Phenotypes  2018.11 

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    Country：Japan  

  • 内視鏡画像の臓器等への自動分類 Invited

    内田誠一

    消化器内視鏡領域におけるAI研究実績報告会  2018.11 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：神戸商工会議所　神商ホール   Country：Japan  

  • グラフカットとCNNを用いたマウス胚領域分割

    原田大輔, 備瀬竜馬, 岡 早苗, Timothy Francis Day, 藤森俊彦, 内田誠一

    電子情報通信学会医用画像研究会  2018.11 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：兵庫県立大学 神戸情報科学キャンパス   Country：Japan  

  • Biomedical image analysis as an interesting machine learning task Invited

    Shonan Meeting No.128 Workshop on Patient Similitude: Combining Histopathological Images & Multiple-Scale Molecular Phenotypes  2018.11 

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    Language：English   Presentation type：Oral presentation (general)  

    Country：Japan  

  • 花押類似検索のための畳み込みオートエンコーダによる画像特徴抽出 International conference

    鬼塚洋輔, 大山航, 山田太造, 井上聡, 内田誠一

    人文科学とコンピュータシンポジウム  2018.12 

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    Language：Japanese  

    Venue：東京大学地震研究所   Country：Japan  

  • 深層学習の原理と応用について Invited

    内田誠一

    平成30年 情報処理学会九州支部若手の会セミナー  2018.12 

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    Language：Japanese  

    Venue：国民宿舎 虹ノ松原ホテル   Country：Japan  

  • 機械可読時代における文字科学の創成と応用展開 Invited

    内田誠一

    情報系Winterfesta episode4  2018.12 

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    Language：Japanese  

    Venue：学術総合センター   Country：Japan  

  • 書籍タイトルフォントのデザイン解析

    唐松拓郎, 川口維文, 品原悠杜, 内田誠一

    人文科学とコンピュータシンポジウム  2018.12 

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    Language：Japanese  

    Venue：東京大学地震研究所   Country：Japan  

  • 投手の打ちづらさとは何か ～ 機械学習に基づく投球印象解析 ～

    角 淳之介, 末廣大貴, 加藤貴昭, 内田誠一

    映像情報メディア学会・メディア工学研究会/スポーツ情報処理時限研究会  2018.12 

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    Language：Japanese  

    Venue：電気通信大学   Country：Japan  

  • 手書き文字と活字の境界を探る

    森みづき, 中村俊貴, 早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2018.12 

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    Language：Japanese  

    Venue：東北大学   Country：Japan  

  • 情景内文字のCNNによる拡大

    中村俊貴, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2018.12 

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    Language：Japanese  

    Venue：東北大学   Country：Japan  

  • 花押類似検索のための畳み込みオートエンコーダによる画像特徴抽出 International conference

    鬼塚洋輔, 大山航, 山田太造, 井上聡, 内田誠一

    人文科学とコンピュータシンポジウム  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東京大学地震研究所   Country：Japan  

  • 書籍タイトルフォントのデザイン解析

    唐松拓郎, 川口維文, 品原悠杜, 内田誠一

    人文科学とコンピュータシンポジウム  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東京大学地震研究所   Country：Japan  

  • 九州大学におけるデータサイエンス教育研究の取組 Invited

    内田誠一

    リベラルサイエンス教育開発FD  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州大学   Country：Japan  

  • データサイエンス概論第一 Invited

    内田誠一

    ふくおかiST システム開発技術カレッジ  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：福岡システムLSI総合開発センター   Country：Japan  

  • 深層学習の原理と応用について Invited

    内田誠一

    平成30年 情報処理学会九州支部若手の会セミナー  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：国民宿舎 虹ノ松原ホテル   Country：Japan  

  • 手書き文字と活字の境界を探る

    森みづき, 中村俊貴, 早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東北大学   Country：Japan  

  • 情景内文字のCNNによる拡大

    中村俊貴, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東北大学   Country：Japan  

  • 投手の打ちづらさとは何か ～ 機械学習に基づく投球印象解析 ～

    角 淳之介, 末廣大貴, 加藤貴昭, 内田誠一

    映像情報メディア学会・メディア工学研究会/スポーツ情報処理時限研究会  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：電気通信大学   Country：Japan  

  • 機械可読時代における文字科学の創成と応用展開 Invited

    内田誠一

    情報系Winterfesta episode4  2018.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：学術総合センター   Country：Japan  

  • 文字を含む情景画像の異種CNN融合による超解像

    中尾 亮, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.1 

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    Language：Japanese  

    Venue：京都テルサ   Country：Japan  

  • 弱教師学習問題における最適局所特徴抽出および樹状突起スパイン検出への応用

    八尋俊希, 末廣大貴, 本館利佳, 鈴木利治, 内田誠一

    電子情報通信学会医用画像研究会  2019.1 

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    Language：Japanese  

    Venue：沖縄県青年会館   Country：Japan  

  • 共有潜在空間を利用した手書き文字のオンライン・オフライン変換

    角 太智, 早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.1 

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    Language：Japanese  

    Venue：京都テルサ   Country：Japan  

  • データを介したオープンサイエンスへ～ 九州大学におけるデータサイエンスの展開 Invited

    内田誠一

    九州大学病院臨床観察研究支援事業COS3 観察研究ノススメ 第8回シンポジウム  2019.1 

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    Language：Japanese  

    Venue：九州大学   Country：Japan  

  • CNNによるテクスチャ認識における周波数特徴の有効性の検証

    川路啓太, 早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.1 

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    Language：Japanese  

    Venue：京都テルサ   Country：Japan  

  • データを介したオープンサイエンスへ～ 九州大学におけるデータサイエンスの展開 Invited

    内田誠一

    九州大学病院臨床観察研究支援事業COS3 観察研究ノススメ 第8回シンポジウム  2019.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州大学   Country：Japan  

  • 文字を含む情景画像の異種CNN融合による超解像

    中尾 亮, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：京都テルサ   Country：Japan  

  • 共有潜在空間を利用した手書き文字のオンライン・オフライン変換

    角 太智, 早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：京都テルサ   Country：Japan  

  • CNNによるテクスチャ認識における周波数特徴の有効性の検証

    川路啓太, 早志英朗, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：京都テルサ   Country：Japan  

  • 弱教師学習問題における最適局所特徴抽出および樹状突起スパイン検出への応用

    八尋俊希, 末廣大貴, 本館利佳, 鈴木利治, 内田誠一

    電子情報通信学会医用画像研究会  2019.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：沖縄県青年会館   Country：Japan  

  • Deep learningの医用画像解析応用 Invited

    内田誠一

    OSSユーザーのための勉強会 「#26 デジタル トランスフォーメーション ～ 社会・産業・生活を変える技術」  2019.2 

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    Language：Japanese  

    Venue：ベルサール八重洲   Country：Japan  

  • Deep learningの医用画像解析応用 Invited

    内田誠一

    OSSユーザーのための勉強会 「#26 デジタル トランスフォーメーション ～ 社会・産業・生活を変える技術」  2019.2 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：ベルサール八重洲   Country：Japan  

  • 関節の非同期DPマッチングを用いたスポーツ動作解析

    角 淳之介, 永田聡典, 加藤貴昭, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.3 

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    Language：Japanese  

    Venue：電気通信大学   Country：Japan  

  • 画像情報学と深層学習 Invited

    内田誠一

    九州半導体・エレクトロニクイノベーション協議会 「大学シーズ発信：AIによる工場の自動化と生産設備・機器設計への活用」  2019.3 

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    Language：Japanese  

    Venue：ハイアットリージェンシー福岡   Country：Japan  

  • 深層学習によるカキ果実における生理障害の画像診断および判断要因の可視化

    赤木剛士, 黒木陵平, 大西信徳, 鈴木哲也, 新川猛, 田尾龍太郎, 内田誠一, 伊勢武史

    園芸学会 平成31年度春季大会  2019.3 

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    Language：Japanese  

    Venue：明治大学生田キャンパス   Country：Japan  

  • 動的計画法を用いた内視鏡画像系列クラスタリング

    原田翔太, 早志英朗, 備瀬⻯馬, 田中聖人, Qier Meng, 内田誠一

    生体画像と医用人工知能研究会 第1回若手発表会  2019.3 

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    Language：Japanese  

    Venue：群馬県立県⺠健康科学大学   Country：Japan  

  • 九州大学におけるデータサイエンス教育 Invited

    内田誠一

    九大-理研-福岡市・ISIT三者連携フォーラム「データ×サイエンス×ビジネス ～AI・デジタルで社会を変える」  2019.3 

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    Language：Japanese  

    Venue：ハイアットリージェンシー福岡   Country：Japan  

  • バイオイメージインフォマティクスと機械学習 Invited

    内田誠一

    第18回日本再生医療学会総会「シンポジウム33 AIを用いた幹細胞・発生研究」  2019.3 

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    Language：Japanese  

    Venue：神戸国際会議場   Country：Japan  

  • なぜデータサイエンスか? Invited

    内田誠一

    九州大学 CSTIPS・福岡県調査統計課 「EBPM(エビデンスに基づく政策形成)セミナー」  2019.3 

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    Language：Japanese  

    Venue：九州大学   Country：Japan  

  • Contrastive-LRPの改良とその多クラス分類可視化応用

    黒木陵平, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.3 

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    Language：Japanese  

    Venue：電気通信大学   Country：Japan  

  • 九州大学におけるデータサイエンス教育 Invited

    内田誠一

    九大-理研-福岡市・ISIT三者連携フォーラム「データ×サイエンス×ビジネス ～AI・デジタルで社会を変える」  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：ハイアットリージェンシー福岡   Country：Japan  

  • 動的計画法を用いた内視鏡画像系列クラスタリング

    原田翔太, 早志英朗, 備瀬⻯馬, 田中聖人, Qier Meng, 内田誠一

    生体画像と医用人工知能研究会 第1回若手発表会  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：群馬県立県⺠健康科学大学   Country：Japan  

  • Contrastive-LRPの改良とその多クラス分類可視化応用

    黒木陵平, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：電気通信大学   Country：Japan  

  • 関節の非同期DPマッチングを用いたスポーツ動作解析

    角　淳之介, 永田聡典, 加藤貴昭, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：電気通信大学   Country：Japan  

  • 画像情報学と深層学習 Invited

    内田誠一

    九州半導体・エレクトロニクイノベーション協議会 「大学シーズ発信：AIによる工場の自動化と生産設備・機器設計への活用」  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：ハイアットリージェンシー福岡   Country：Japan  

  • なぜデータサイエンスか? Invited

    内田誠一

    九州大学 CSTIPS・福岡県調査統計課 「EBPM(エビデンスに基づく政策形成)セミナー」  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (invited, special)  

    Venue：九州大学   Country：Japan  

  • バイオイメージインフォマティクスと機械学習 Invited

    内田誠一

    第18回日本再生医療学会総会「シンポジウム33 AIを用いた幹細胞・発生研究」  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：神戸国際会議場   Country：Japan  

  • 深層学習によるカキ果実における生理障害の画像診断および判断要因の可視化

    赤木剛士, 黒木陵平, 大西信徳, 鈴木哲也, 新川猛, 田尾龍太郎, 内田誠一, 伊勢武史

    園芸学会 平成31年度春季大会  2019.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：明治大学生田キャンパス   Country：Japan  

  • バイオイメージ・インフォマティクスの可能性 Invited

    内田誠一

    第24回生物工学懇話会  2019.5 

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    Language：Japanese  

    Venue：千里ライフサイエンスセンター   Country：Japan  

  • Classification with imbalanced cloud data using deep convolutional neural network

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama and Seiichi Uchida

    Japan Geoscience Union Meeting 2019  2019.5 

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    Language：English  

    Country：Japan  

  • Cardiotocogramの識別に基づく胎児の状態推定

    原田翔太, 早志英朗, 古賀俊介, 重見大介, 柴田綾子, 吉田昌義, 蓮尾泰之, 内田誠一

    電子情報通信学会技術研究報告  2019.5 

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    Language：Japanese  

    Venue：名古屋工業大学   Country：Japan  

  • バイオイメージ・インフォマティクスの可能性 Invited

    内田誠一

    第24回生物工学懇話会  2019.5 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：千里ライフサイエンスセンター   Country：Japan  

  • Cardiotocogramの識別に基づく胎児の状態推定

    原田翔太, 早志英朗, 古賀俊介, 重見大介, 柴田綾子, 吉田昌義, 蓮尾泰之, 内田誠一

    電子情報通信学会技術研究報告  2019.5 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：名古屋工業大学   Country：Japan  

  • Classification with imbalanced cloud data using deep convolutional neural network

    Daisuke Matsuoka, Masuo Nakano, Daisuke Sugiyama and Seiichi Uchida

    Japan Geoscience Union Meeting 2019  2019.5 

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    Language：English   Presentation type：Oral presentation (general)  

    Country：Japan  

  • 文字情報に潜む意図を探る Invited

    内田誠一

    第25回画像センシングシンポジウム  2019.6 

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    Language：Japanese  

    Venue：パシフィコ横浜   Country：Japan  

  • 文字情報に潜む意図を探る Invited

    内田誠一

    第25回画像センシングシンポジウム  2019.6 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：パシフィコ横浜   Country：Japan  

  • 画像に基づく言語変換

    馬場 康平, Brian Kenji Iwana, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese  

    Venue：グランキューブ大阪   Country：Japan  

  • 情景内単語と物体の共起性に関する実験的考察

    竹下 孔喜, 塩山 惇太郎, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese  

    Venue：グランキューブ大阪   Country：Japan  

  • 大腸の画像診断： 大腸生検の病理画像解析および大腸の内視鏡画像解析 Invited

    内田誠一

    日本医用画像工学会大会 シンポジウム1  2019.7 

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    Language：Japanese  

    Venue：奈良春日野国際フォーラム   Country：Japan  

  • オンライントラッカの統合について

    ソン ホン, 末廣大貴, 内田誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese  

    Venue：グランキューブ大阪   Country：Japan  

  • オンラインエキスパート選択問題としての適応的学習率調整

    満尾 成亮, 末廣 大貴, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese  

    Venue：グランキューブ大阪   Country：Japan  

  • Learning Convolutional Autoencoders with a Metric Constraint

    Yosuke Onitsuka, Wataru Ohyama, Seiichi Uchida

    2019.7 

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    Language：English  

    Country：Japan  

  • Endoscopic Image Clustering Based on Temporal Ordering Information

    Shota Harada, Hideaki Hayashi, Ryoma Bise, Qier Meng, Kiyohito Tanaka, Seiichi Uchida

    2019.7 

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    Language：English  

    Country：Japan  

  • CNNを用いたアンサンブル学習による画像分類

    杉原 麻美子, 早志 英朗, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese  

    Venue：グランキューブ大阪   Country：Japan  

  • 大腸の画像診断： 大腸生検の病理画像解析および大腸の内視鏡画像解析 Invited

    内田誠一

    日本医用画像工学会大会 シンポジウム1  2019.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：奈良春日野国際フォーラム   Country：Japan  

  • Endoscopic Image Clustering Based on Temporal Ordering Information

    Shota Harada, Hideaki Hayashi, Ryoma Bise, Qier Meng, Kiyohito Tanaka, Seiichi Uchida

    2019.7 

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    Language：English   Presentation type：Oral presentation (general)  

    Country：Japan  

  • オンラインエキスパート選択問題としての適応的学習率調整

    満尾 成亮, 末廣 大貴, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：グランキューブ大阪   Country：Japan  

  • Learning Convolutional Autoencoders with a Metric Constraint

    Yosuke Onitsuka, Wataru Ohyama, Seiichi Uchida

    2019.7 

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    Language：English   Presentation type：Oral presentation (general)  

    Country：Japan  

  • 画像に基づく言語変換

    馬場 康平, Brian Kenji Iwana, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：グランキューブ大阪   Country：Japan  

  • オンライントラッカの統合について

    ソン ホン, 末廣大貴, 内田誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：グランキューブ大阪   Country：Japan  

  • CNNを用いたアンサンブル学習による画像分類

    杉原 麻美子, 早志 英朗, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：グランキューブ大阪   Country：Japan  

  • 情景内単語と物体の共起性に関する実験的考察

    竹下 孔喜, 塩山 惇太郎, 内田 誠一

    画像の認識・理解シンポジウム  2019.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：グランキューブ大阪   Country：Japan  

  • 画像情報学と機械学習 Invited

    内田誠一

    先端バイオイメージング支援プラットフォーム(ABiS)主催： AIによる生物画像解析トレーニングコース  2019.8 

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    Language：Japanese  

    Venue：熊本大学黒髪キャンパス   Country：Japan  

  • Open Research Directions of Document Analysis and Recognition Invited International conference

    3rd IAPR Summer School on Document Analysis (SSDA2019): Deep Learning Applications for Document Analysis  2019.8 

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    Language：English  

    Country：Pakistan  

  • Machine learning for pattern recognition: From the nearest-neighbor method to deep learning Invited International conference

    One Day Internaiotnal Workshop on Pattern Recognition Application 2019  2019.8 

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    Language：English  

    Country：Pakistan  

  • Machine learning for pattern recognition: From the nearest-neighbor method to deep learning Invited International conference

    One Day Internaiotnal Workshop on Pattern Recognition Application 2019  2019.8 

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    Language：English   Presentation type：Oral presentation (general)  

    Country：Pakistan  

  • Open Research Directions of Document Analysis and Recognition Invited International conference

    3rd IAPR Summer School on Document Analysis (SSDA2019): Deep Learning Applications for Document Analysis  2019.8 

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    Language：English   Presentation type：Oral presentation (general)  

    Country：Pakistan  

  • 画像情報学と機械学習 Invited

    内田誠一

    先端バイオイメージング支援プラットフォーム(ABiS)主催： AIによる生物画像解析トレーニングコース  2019.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：熊本大学黒髪キャンパス   Country：Japan  

  • 画像情報学と深層学習（ディープラーニング）の現状 Invited

    内田誠一

    園芸学会 令和元年度（2019年度）秋季大会  2019.9 

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    Language：Japanese  

    Venue：島根大学 教養講義室棟1号館   Country：Japan  

  • 画像ラベル付け簡易化のためのソフト制約つきクラスタリング手法の提案

    備瀬竜馬, 安部健太郎, 早志英朗, 田中聖人, 内田誠一

    電子情報通信学会技術研究報告  2019.9 

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    Language：Japanese  

    Venue：岡山大学   Country：Japan  

  • 全分野横断・全学年縦断のデータサイエンス教育の効率的推進 Invited

    内田誠一

    日本工学教育協会 21世紀リベラルアーツ調査研究委員会 講演会  2019.9 

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    Language：Japanese  

    Venue：芝浦工業大学芝浦キャンパス   Country：Japan  

  • 人工知能の基礎、応用「情報側の視点から」 Invited

    内田誠一

    日本医療研究開発機構(AMED) 医療研究開発業務研修  2019.9 

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    Language：Japanese  

    Venue：日本医療研究開発機構   Country：Japan  

  • マルチタスク学習による大腸内視鏡画像の部位及び所見分類

    安部健太郎, 早志英朗, 備瀬竜馬, 河村卓二., 碕山直邦, 田中聖人, 内田誠一

    電子情報通信学会技術研究報告  2019.9 

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    Language：Japanese  

    Venue：岡山大学   Country：Japan  

  • 安定結婚アルゴリズムによる細胞内中心体のトラッキング

    川原祐樹, 備瀬竜馬, 木村 暁, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • ランダム重みを持つニューラルネットワークの解析

    久保田祥平, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 機械学習を用いた手書き文字の筆跡予測

    山縣将貴, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 動画像中の文字トラッキングの試み

    坂口翔太, 加藤 淳, 後藤真孝, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 投球データによる野球の勝敗予測

    川上祐司, 原田翔太, 末廣大貴, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 強化学習による文字の自動筆記の検討

    神田敬佑, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 深層学習による子宮頸癌のクラス分類

    荒木健吾, 徳永宏樹, 備瀬竜馬, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 局所パターン生成の検討

    Chean Fei Shee, 末廣大貴, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 最大プーリング平滑化の効果検証

    緒續隆人, Yuchen Zheng, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：九州工業大学   Country：Japan  

  • 全分野横断・全学年縦断のデータサイエンス教育の効率的推進 Invited

    内田誠一

    日本工学教育協会　21世紀リベラルアーツ調査研究委員会 講演会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：芝浦工業大学芝浦キャンパス   Country：Japan  

  • マルチタスク学習による大腸内視鏡画像の部位及び所見分類

    安部健太郎, 早志英朗, 備瀬竜馬, 河村卓二., 碕山直邦, 田中聖人, 内田誠一

    電子情報通信学会技術研究報告  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：岡山大学   Country：Japan  

  • 人工知能の基礎、応用「情報側の視点から」 Invited

    内田誠一

    日本医療研究開発機構(AMED) 医療研究開発業務研修  2019.9 

    　More details

    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：日本医療研究開発機構   Country：Japan  

  • 画像ラベル付け簡易化のためのソフト制約つきクラスタリング手法の提案

    備瀬竜馬, 安部健太郎, 早志英朗, 田中聖人, 内田誠一

    電子情報通信学会技術研究報告  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：岡山大学   Country：Japan  

  • 画像情報学と深層学習（ディープラーニング）の現状 Invited

    内田誠一

    園芸学会 令和元年度（2019年度）秋季大会  2019.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：島根大学 教養講義室棟1号館   Country：Japan  

  • 配色情報を統合した単語分散表現の生成と分析

    生駒真也, 内田誠一

    電子情報通信学会技術研究報告  2019.10 

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    Language：Japanese  

    Venue：東京大学生産技術研究所   Country：Japan  

  • あなたがいま読んでいるものは文字です ～ 画像情報学から見た文字研究のこれから ～ Invited

    内田誠一

    電子情報通信学会技術研究報告  2019.10 

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    Language：Japanese  

    Venue：東京大学生産技術研究所   Country：Japan  

  • 配色情報を統合した単語分散表現の生成と分析

    生駒真也, 内田誠一

    電子情報通信学会技術研究報告  2019.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東京大学生産技術研究所   Country：Japan  

  • あなたがいま読んでいるものは文字です ～ 画像情報学から見た文字研究のこれから ～ Invited

    内田誠一

    電子情報通信学会技術研究報告  2019.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：東京大学生産技術研究所   Country：Japan  

  • 基調講演：文字認識研究の過去・現在・未来 Invited

    内田誠一

    日本文化とAIシンポジウム2019～AIがくずし字を読む時代がやってきた  2019.11 

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    Language：Japanese  

    Venue：一橋講堂   Country：Japan  

  • 九州大学のデータサイエンス教育 Invited

    内田誠一

    ｢数理･データサイエンスを活かした地域産業人材の育成に向けたカリキュラム･教材の開発｣事業キックオフシンポジウム  2019.11 

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    Language：Japanese  

    Venue：宮崎大学創立330記念交流会館   Country：Japan  

  • 基調講演：文字認識研究の過去・現在・未来 Invited

    内田誠一

    日本文化とAIシンポジウム2019～AIがくずし字を読む時代がやってきた  2019.11 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：一橋講堂   Country：Japan  

  • 九州大学のデータサイエンス教育 Invited

    内田誠一

    ｢数理･データサイエンスを活かした地域産業人材の育成に向けたカリキュラム･教材の開発｣事業キックオフシンポジウム  2019.11 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：宮崎大学創立330記念交流会館   Country：Japan  

  • 未学習CNNの反復的な統合による画像分類

    杉原麻美子, 早志英朗, 内田誠一

    電子情報通信学会技術研究報告  2019.12 

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    Language：Japanese  

    Venue：大分大学   Country：Japan  

  • 未学習CNNの反復的な統合による画像分類

    杉原麻美子, 早志英朗, 内田誠一

    電子情報通信学会技術研究報告  2019.12 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：大分大学   Country：Japan  

  • 画像情報学とAI Invited

    内田誠一

    日本工学アカデミー 九州支部 工業 高等専門学校出張 講演会 「AI 応用の最先端と今後展望」  2020.1 

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    Language：Japanese  

    Venue：鹿児島高専   Country：Japan  

  • 情景内文字情報を用いた情景認識

    塩山 惇太郎, 内田 誠一

    情報処理学会コンピュータビジョンとイメージメディア研究会  2020.1 

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    Language：Japanese  

    Venue：奈良先端科学技術大学   Country：Japan  

  • AIと医用画像解析 Invited

    内田誠一

    第32回 骨・関節疾患シンポジウム  2020.1 

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    Language：Japanese  

    Venue：アクロス福岡 国際会議場   Country：Japan  

  • 画像情報学とAI Invited

    内田誠一

    日本工学アカデミー 九州支部 工業 高等専門学校出張 講演会 「AI 応用の最先端と今後展望」  2020.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：鹿児島高専   Country：Japan  

  • 情景内文字情報を用いた情景認識

    塩山 惇太郎, 内田 誠一

    情報処理学会コンピュータビジョンとイメージメディア研究会  2020.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：奈良先端科学技術大学   Country：Japan  

  • AIと医用画像解析 Invited

    内田誠一

    第32回 骨・関節疾患シンポジウム  2020.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：アクロス福岡 国際会議場   Country：Japan  

  • 識別と生成のハイブリッドニューラルネットワーク

    早志英朗, 内田誠一

    電子情報通信学会技術研究報告  2020.3 

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    Language：Japanese  

    Venue：オンライン   Country：Japan  

  • 深層学習を用いた異種文字間のフォント同一性判定

    原口大地, 原田翔太, Brian Kenji Iwana, 内田誠一

    電子情報通信学会技術研究報告  2020.3 

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    Language：Japanese  

    Venue：オンライン   Country：Japan  

  • ランキング学習による大腸内視鏡画像の重症度予測

    安部健太郎, Yan Zheng, 早志英朗, 備瀬竜馬, 河村卓二, 碕山直邦, 田中聖人, 内田誠一

    電子情報通信学会2020年総合大会  2020.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：コロナウイルスにより現地開催中止   Country：Japan  

  • 深層学習に基づく柿の早期軟化発生予測

    馬場康平, 増田佳苗, 鈴木茉莉亜, 赤木剛士, 内田誠一

    電子情報通信学会2020年総合大会  2020.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：コロナウイルスにより現地開催中止   Country：Japan  

  • 深層学習モデルを用いたカキ種無し果の予測と判断要因の可視化

    増田佳苗, 鈴木茉莉亜, 馬場康平, 鈴木哲也, 杉浦真由, 新川猛, 内田誠一, 赤木剛士

    園芸学会 令和2年度春季大会  2020.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：コロナウイルスにより現地開催中止   Country：Japan  

  • ゲノムへの深層学習（第1報）:カキゲノムにおける短配列への適用

    赤木剛士, 増田佳苗, 馬場康平, 内田誠一

    園芸学会 令和2年度春季大会  2020.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：コロナウイルスにより現地開催中止   Country：Japan  

  • 深層学習を用いた異種文字間のフォント同一性判定

    原口大地, 原田翔太, Brian Kenji Iwana, 内田誠一

    電子情報通信学会技術研究報告  2020.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 識別と生成のハイブリッドニューラルネットワーク

    早志英朗, 内田誠一

    電子情報通信学会技術研究報告  2020.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • クラスの存在を利用した時系列予測とその手書きパターンへの応用

    山縣将貴, 早志英朗, 内田誠一

    電子情報通信学会技術研究報告  2020.5 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Class-Guided Handwriting Prediction with Uncertainty

    Masaki Yamagata, Hideaki Hayashi, Seiichi Uchida

    2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • 深層特徴を用いたリジェクション学習

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • GANを用いた局所パターン生成

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 異種文字間のフォント同一性判定

    原口大地, 原田翔太, Brian Kenji Iwana, 内田誠一

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 内視鏡画像列に関する事前知識を用いた自己制約クラスタリング

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 任意のオンライントラッカの統合法

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 模倣学習による手書き生成

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 日本語テキストの属性認識に向けて

    下田和, 原口大地, 山口光太, 内田誠一

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 単位初期化による深層パーセプトロン学習：ヤコビ行列を用いた誤差逆伝播に関する考察

    久保田祥平, 早志英朗, 早瀬友裕, 内田誠一

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 識別・生成のハイブリッドモデルと弱教師あり学習への応用

    早志英朗, 内田誠一

    画像の認識・理解シンポジウム  2020.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 深層ニューラルネットワーク内部でのデータ改ざん検出の試み

    亀澤祥平, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • ロゴ画像の特徴解析

    西 進太朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • オンライン予測による画像分類器の識別率の制御

    本田康祐, 内田誠一, 末廣大貴

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 複数モダリティを対象とした表現学習

    松尾信之介, Brian Kenji Iwana, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 表紙画像生成

    張 文升, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 映画ポスターにおけるタイトル画像解析

    辻 海元, 原口大地, 内田誠一, Brian Kenji Iwana

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • フォントのクオリティに関する評価関数の提案

    姜 志勲, 原口大地, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 特定印象を考慮した文字フォントの生成に向けた試み

    松田征也, 早志英朗, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 書籍表紙画像のタイトル部自動生成の試み

    宮薗大雅, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Venue：オンライン   Country：Japan  

  • フォントの印象分析

    上田将矢, 原口大地, 内田誠一

    電気・情報関係学会九州支部連合大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 作物ゲノムへのディープラーニングによるcis配列デコーディング

    赤木剛士, 増田佳苗, 内田誠一

    日本植物学会第84回大会  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 情景内単語と物体の共起に関する大規模解析 ～ ラベルとメッセージの識別を目指して ～

    竹下孔喜, 塩山惇太郎, 内田誠一

    電子情報通信学会技術研究報告  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Future Challenges in Handwriting Recognition Invited International conference

    Seiichi Uchida

    The 17th International Conference on Frontiers of Handwriting Recognition (ICFHR2020)  2020.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • 作物ゲノムへの深層学習：短配列への適用と可能性

    赤木剛士, 内田誠一

    日本育種学会第138回講演会  2020.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • キウイフルーツ果実における成熟応答cisモチーフのゲノムワイド探索

    桒田恵理子, 藤田尚子, 竹下孔喜, 牛島幸一郎, 久保康隆, 内田誠一, 赤木剛士

    日本育種学会第138回講演会  2020.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 深層学習によるカキ果実画像からの早期軟化予測モデル

    鈴木茉莉亜, 増田佳苗, 竹下孔喜, 朝隈英昭, 鈴木哲也, 杉浦真由, 新川猛, 内田誠一, 赤木剛士

    日本育種学会第138回講演会  2020.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 簡易な相対アノテーションに基づく潰瘍性大腸炎の重症度分類

    門田健明, 安部健太郎, 備瀬竜馬, 河村卓二, 碕山直邦, 田中聖人, 内田誠一

    電子情報通信学会技術研究報告  2020.10 

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    Venue：オンライン   Country：Japan  

  • 内視鏡画像のMayo分類のための分離された特徴表現の獲得

    原田翔太, 早志英朗, 備瀬竜馬, 河村卓二, 碕山直邦, 田中聖人, 内田誠一

    電子情報通信学会技術研究報告  2020.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Deep Setsの挙動解析 ～ 文字画像を対象とした可視化 ～

    神田敬佑, 内田誠一

    電子情報通信学会技術研究報告  2020.10 

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    Venue：オンライン   Country：Japan  

  • 正則化プーリング

    緒續隆人, 早志英朗, Zheng Yuchen, 内田誠一

    電子情報通信学会技術研究報告  2020.10 

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    Venue：オンライン   Country：Japan  

  • リリックビデオにおける歌詞内単語の動きの抽出と分類

    坂口翔太, 加藤 淳, 後藤真孝, 内田誠一

    電子情報通信学会技術研究報告  2020.10 

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    Venue：オンライン   Country：Japan  

  • 深層パーセプトロンの単位初期化に基づく中間層の貢献度と尤度の解析

    久保田祥平, 早志英朗, 早瀬友裕, 内田誠一

    電子情報通信学会技術研究報告  2020.12 

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    Venue：オンライン   Country：Japan  

  • AIとは何か? : 入門編 (特集 さあ、AIを始めよう : 土木工学へのAI導入のススメ)—An intuitive introduction of AI

    内田 誠一

    土木学会誌  2021.1 

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    Language：Japanese  

    Country：Japan  

  • 単語分散表現におけるフォントスタイル情報の利用

    原口大地, 下田和, 内田誠一

    情報処理学会コンピュータビジョンとイメージメディア研究会 (コロナウイルスによりオンライン開催)  2021.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Visual Design Analysis with Machine Learning Invited International conference

    Seiichi Uchida

    2021.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Playing with Visible Texts around Us Invited International conference

    Seiichi Uchida

    RIEC International Symposium: Symposium of Yotta Informatics Research Platform for Yotta-Scale Data Science 2021  2021.3 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Beyond 100% - Open research in Document Analysis Invited International conference

    Seiichi Uchida

    The Annual Workshop of the Swedish Artificial Intelligence Society(SAIS2021)  2021.6 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • すべての学術を結ぶデータサイエンス教育を目指して Invited

    内田誠一

    精密工学会 画像応用技術専門委員会  2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Analysis of Historical Changes in the Fonts on Movie Posters

    Kaigen Tsuji, Seiichi Uchida, Brian Kenji Iwana

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Towards Book Cover Design via Layout Graphs

    Wensheng Zhang, Seiichi Uchida, Brian Kenji Iwana

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • フォントスタイル情報が単語分散表現に与える影響の調査

    原口大地, 内田誠一

    画像の認識・理解シンポジウム  2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Part-based Analysis to Understand Font Impression

    Masaya Ueda, Akisato Kimura, Seiichi Uchida

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • テキストをエディット可能に

    画像の認識・理解シンポジウム  2021.7 

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    Venue：オンライン   Country：Japan  

  • Deep Metric Learning Based on Attention Model for Multivariate Time Series

    Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan, Akisato Kimura, Kunio Kashino, Brian Kenji Iwana, Seiichi Uchida

    2021.7 

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    Country：Japan  

  • Disentangled Representation Learning with Temporal Continuity for Ulcerative Colitis Classification

    Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Meta-learning of Pooling Layers for Few-shot Recognition

    Takato Otsuzuki, Heon Song, Seiichi Uchida, Hideaki Hayashi

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Generating Font Images with Specific Impressions

    Seiya Matsuda, Akisato Kimura, Seiichi Uchida

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • Relationship Analysis between Logos and Their Followers

    Takeaki Kadota, Shintaro Nishi, Seiichi Uchida

    2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • GANによる多層画像生成

    画像の認識・理解シンポジウム  2021.7 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Clustering Analysis of Images, Object Labels, and Scene Texts

    2021.7 

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    Country：Japan  

  • 識別器の斟酌学習

    本田康祐, 内田誠一，末廣大貴

    電子情報通信学会技術研究報告  2021.8 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • I Have Two Dreams Invited International conference

    Seiichi Uchida

    The Third Future of Document Image Analysis Workshop (FDAR2021)  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • オープンサイエンス＆オープンエデュケーション with オープンマインド： 九州大学における「全分野横断・全学年縦断型数理・データサイエンス教育」 Invited

    内田誠一

    IDE大学協会 近畿支部 セミナー 2021年度「データサイエンス教育の必修化を巡って －文理融合型教育・高大接続・大学間連携－」  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 深層学習によるカキ果実の早期軟化予測と初期生理反応の特徴化

    鈴木茉莉亜, 増田佳苗, 竹下孔喜, 朝隈英昭, 杉浦真由, 鈴木哲也, 新川猛, 久保康隆, 牛島幸一郎, 内田誠一, 赤木剛士

    園芸学会令和3年度秋季大会  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 深層学習によるトマト果実の遺伝子発現デザインへ向けたゲノムワイドcis デコーディング

    赤木剛士, 増田佳苗, 桒田恵理子, 竹下孔喜, 川勝泰二, 有泉亨, 久保康隆, 牛島幸一郎, 内田誠一

    園芸学会令和3年度秋季大会  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • 深層学習によるキウイフルーツ果実における成熟応答cis モチーフのゲノムワイド探索

    桒田恵理子, 竹下孔喜, 藤田尚子, 牛島幸一郎, 久保康隆, 別府賢治, 片岡郁雄, 内田誠一, 赤木剛士

    園芸学会令和3年度秋季大会  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Logo Images Classification

    2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • 単語の意味と画像の共潜在空間埋め込み

    安河内直哉, 松尾信之介, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Venue：オンライン   Country：Japan  

  • 情景内文字とキャプションの相関解析

    中村亘岐, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Venue：オンライン   Country：Japan  

  • 異なるトレーニング順序でのモデル圧縮

    沈 毅誠, Brian Iwana, Seiichi Uchida

    電気・情報関係学会九州支部連合大会  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Invariant Information Clusteringの挙動解析

    北島和樹, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Venue：オンライン   Country：Japan  

  • 画像照合を用いた手書き数式認識結果の検証

    KhayTze Peong, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Venue：オンライン   Country：Japan  

  • Transformerを用いたアウトライン文字認識

    永田悠祐, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Venue：オンライン   Country：Japan  

  • Vision Transformerを用いた多フォント文字認識

    大峠仁輝, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Venue：オンライン   Country：Japan  

  • Vision Transformerを用いた数字画像の欠損補間

    中鶴 慧, 内田誠一

    電気・情報関係学会九州支部連合大会  2021.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Education and Research of Mathematics, Data Science, and Artificial Intelligence in Kyushu University Invited International conference

    Seiichi Uchida

    Kyushu University Institute for Asian and Oceanian Studies (Q-AOS) Brown Bag Seminar Series #24  2021.10 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Country：Japan  

  • クラス情報を考慮したEnergy-based Modelによる時系列予測手法の提案

    山縣将貴, 内田誠一, 早志英朗

    情報処理学会コンピュータビジョンとイメージメディア研究会  2022.1 

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    Language：Japanese   Presentation type：Oral presentation (general)  

    Venue：オンライン   Country：Japan  

  • Self-Attentionによる非局所構造の利用状況解析

    大峠仁輝, 内田誠一

    電子情報通信学会2022年総合大会  2022.3 

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    Venue：オンライン   Country：Japan  

  • 深層学習によるキウイフルーツ果実成熟応答における新規cis-trans 相互作用ネットワークの解明

    桒田 恵理子, 竹下 孔喜, 藤田 尚子, 牛島 幸一郎, 久保 康隆, 内田 誠一, 赤木 剛

    日本育種学会第141回講演会  2022.3 

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    Venue：オンライン   Country：Japan  

  • オープンエデュケーション&オープンサイエンスwithオープンマインド : 九州大学におけるデータサイエンス教育—「数理・データサイエンス・AI教育プログラム(リテラシーレベル)プラス」選定校における教育実践取組みの紹介(その1)

    内田 誠一

    大学教育と情報  2022.6 

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    Language：Japanese  

    Country：Japan  

  • 集合距離学習による手書き数式認識結果の事後補正

    ピョン ケイジ, 松尾 信之介, 内田 誠一

    画像の認識・理解シンポジウム  2022.7 

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    Language：Others  

    Country：Other  

  • 識別器の斟酌学習

    本田 康祐, 内田 誠一, 末廣 大貴

    画像の認識・理解シンポジウム  2022.7 

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    Country：Other  

  • 画素レベルVision Transformerによるフォント画像の欠損補完

    中鶴 慧, 内田 誠一

    画像の認識・理解シンポジウム  2022.7 

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    Country：Other  

  • 多目的最適化問題の一意解のための特異点論応用

    内田 誠一, 加葉田 雄太朗

    画像の認識・理解シンポジウム  2022.7 

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    Country：Other  

  • 単語画像から言語情報を取り除けるか？

    安河内 直哉, 原口 大地, 内田 誠一

    画像の認識・理解シンポジウム  2022.7 

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  • コンテキストを考慮したテキストデザインの自動推薦

    下田 和, 原口 大地, 内田 誠一, 山口 光太

    画像の認識・理解シンポジウム  2022.7 

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  • Vision Transformerによるパッチベース文字認識

    大峠 仁輝, 内田 誠一

    画像の認識・理解シンポジウム  2022.7 

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  • Transformer for Outline Font

    Yusuke Nagata, Jinki Otao, Daichi Haraguchi, Seiichi Uchida

    2022.7 

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  • Shape-to-Impression Translation for Fonts

    Masaya Ueda, Akisato KImura, Seiichi Uchida

    2022.7 

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  • Semi-Supervised Domain Adaptation for Class-Imbalanced Dataset

    Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata-Rokutan, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida

    2022.7 

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  • Learning Top-Rank Pairs Discloses Reliable Signatures in Writer-Independent Signature Verification

    Xiaotong Ji, Yan Zheng, Daiki Suehiro, Seiichi Uchida

    2022.7 

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  • Impressions-to-Font with Missing Labels

    Seiya Matsuda, Akisato Kimura, Seiichi Uchida

    2022.7 

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  • Attention機構による深層時間ワーピング

    松尾 信之介, Xiaomeng Wu, Gantugs Atarsaikhan, 木村 昭悟, 柏野 邦夫, Brian Kenji Iwana, 内田 誠一

    画像の認識・理解シンポジウム  2022.7 

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  • 数理・データサイエンスを学ぶということ：九州大学の実施例を中心に Invited

    内田誠一

    令和4年度鹿児島大学共通教育センターFD講演会  2022.9 

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  • 医学で役に立ちそうなAIの最新技術・研究の動向 Invited

    内田誠一

    久留米大学バイオ統計学フォーラム  2022.9 

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  • バイオメディカル画像解析に関する Label efficient learning Invited

    内田誠一, 備瀬竜馬

    第31回日本バイオイメージング学会学術集会 「シンポジウム 1 バイオイメージングと情報の協奏」  2022.9 

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  • 或る画像情報学研究者の日々～ 何を面白がって生きているのか？ Invited

    内田誠一

    学術変革領域「挑戦的両性花原理」 若手の会  2022.10 

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  • What are characters (Keynote Speech) Invited

    Seiichi Uchida

    MIRAI2.0 Research & Innovation Week 2022, Parallel Scientific Session: Exciting Trends in Applied AI  2022.11 

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  • オンライン予測理論に基づく擬似ラベル手法によるクラス比率からの学習

    松尾信之介, 備瀬竜馬, 内田誠一, 末廣大貴

    電子情報通信学会パターン認識・メディア理解研究会(RPMU)  2022.12 

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  • 重症度が連続的に変化する医用生成画像を用いたデータ拡張法

    竹崎隼平, 田中聖人, 内田誠一, 門田健明

    電子情報通信学会パターン認識・メディア理解研究会(RPMU)  2022.12 

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  • 事例報告：九州大学および九州・沖縄ブロックにおける実践状況 Invited

    内田誠一

    R4東海地区大学教育研究会研究大会  2022.12 

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  • TextVQAタスクの正答可能性判定

    中村亘岐, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会(RPMU)  2022.12 

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  • Challenges beyond recognition Invited

    Seiichi Uchida

    1st Workshop on Deep Document Understanding (DeepDoc2022), in conjunction with ICFHR2022  2022.12 

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  • 文字とは何か？ --- 画像情報学的視点からの文字の機能解明 Invited

    内田誠一

    中部大学 2022年度 第7回 CMSAIコロキウム  2023.1 

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  • 撮影順序情報を活用した潰瘍性大腸炎分類モデルの提案

    原田翔太, 備瀬竜馬, 田中聖人, 内田誠一

    情報処理学会コンピュータビジョンとイメージメディア研究会  2023.1 

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  • Knowledge Distillation using a Multiple Reference Teacher

    2023.1 

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  • ［ショートペーパー］Deformable Convolutionによる局所変形特徴抽出の試み

    北島和樹, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会(PRMU)  2023.3 

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  • 部分的なラベル比率からの学習

    松尾信之介, 末廣大貴, 内田誠一, 備瀬竜馬

    電子情報通信学会パターン認識・メディア理解研究会(PRMU)  2023.3 

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  • 画像情報学研究者から見た文字の魅力 Invited

    内田誠一

    九州大学「ウェル・ビーイングの実現に貢献する高度人文情報人材養成プログラム：人文学×データサイエンスによる『人文情報学』大学院の設置」発足記念シンポジウム 「データサイエンスと人文学の協働による研究・教育の可能性」  2023.3 

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  • バイオデータ解析に使えそうな機械学習 Invited

    内田誠一

    新学術領域研究シンギュラリティ生物学成果公開シンポジウム  2023.3 

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  • クラス分類性能均衡化のためのBoosting

    斉藤優也, 松尾信之介, 内田誠一, 末廣大貴

    電子情報通信学会 情報論的学習理論と機械学習研究会 (IBIS-ML)  2023.3 

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  • Transformerによる輪郭欠損補完

    永田悠祐, 内田誠一

    電子情報通信学会パターン認識・メディア理解研究会(PRMU)  2023.3 

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  • 画像解析AIに関する最近の動向 Invited

    内田誠一

    九州大学整形外科学教室 第450回 MOC会  2023.4 

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  • 機械学習によるカーニング

    中鶴慧, 内田誠一

    画像の認識・理解シンポジウム  2023.7 

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  • 敵対的攻撃に頑健な文字のデザインをめざして

    片岡蓮太郎, 木村昭悟, 内田誠一

    画像の認識・理解シンポジウム  2023.7 

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  • 情景内単語の選択的消去

    三谷勇人, 木村昭悟, 内田誠一

    画像の認識・理解シンポジウム  2023.7 

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  • 対照学習における注意とその応用

    原口大地, 内田誠一

    画像の認識・理解シンポジウム  2023.7 

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  • 実データと機械学習を組み合わせて楽しむ Invited

    内田誠一

    精密工学会 画像応用技術専門委員会 2023年度第2回研究会  2023.7 

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  • 多目的最適化問題の一意解のための特異点論応用（第二報）

    内田誠一, 加葉田雄太郎

    画像の認識・理解シンポジウム  2023.7 

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  • 事前学習済みU-Netによる画像処理パイプライン

    竹崎隼平, Weizhi Shi, Gantugs Atarsaikhan, 内田誠一

    画像の認識・理解シンポジウム  2023.7 

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  • スタイル特徴は演算可能か?

    近藤徹多, 原口大地, 内田誠一

    画像の認識・理解シンポジウム  2023.7