准教授 ／ 持続的共進化地域創成拠点
|1.||Shigeru Takano, LcwtNet
Lifting complex wavelet layers for constructing a compact DNN model, 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, 10.1109/ISSPIT.2017.8388657, 288-293, 2018.06, [URL], In this paper, a new compact deep neural network (DNN) architecture based on lifting complex wavelets is proposed. The proposed DNN architecture (LcwtNet) is composed of multiple layers in addition to a CNN architecture. Complex wavelet and lifting wavelet layers are introduced as the lower layers of LcwtNet, which can reduce the number of parameters while maintaining high performance similar to that of CNN models. In simulations, the effectiveness of LcwtNet is demonstrated by several test results using the MNIST dataset..
|2.||Naoto Maki, Shohei Nakamura, Shigeru Takano, Yoshihiro Okada, 3D model generation of cattle using multiple depth-maps for ICT agriculture, 11th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2017
Complex, Intelligent, and Software Intensive Systems - Proceedings of the 11th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2017, 10.1007/978-3-319-61566-0_72, 768-777, 2018.01, [URL], This paper proposes new system that generates 3D models of cattle from their multiple depth-maps for estimating their BCS (body condition scores). Various works of the agriculture are almost tedious and the use of advanced ICT is possible to improve such works. Currently, the authors have been studying such an ICT agriculture research whose targets are beef cattle. The goal of this study is to capture 3D shape information of cattle accurately for the estimation of their BCS. BCS are important data for checking whether cattle grow appropriately. However, it is very difficult to capture such information even using a commercial 3D scanner because cattle are animals and always moving. Then, the authors propose the use of multiple depth-maps of a cow simultaneously captured by multiple Kinect sensors at a different viewpoint to generate its 3D model. The problems in this case are the calibration of Kinect sensors and the synchronization of their depth-maps capturing. This paper describes how the authors solve these problems, and it shows several results of actually obtained 3D models of cattle using the proposed system..
|3.||Kensuke Baba, Hiromichi Abe and Shigeru Takano, Using a Simple Electroencephalograph for Activity Recognition of Learners
, IEEJ Transactions on Electronics, Information and Systems, 137, 3, 542-546, 2017.03.
|4.||Shigeru Takano, Maiya Hori, Takayuki Goto, Seiichi Uchida, Ryo Kurazume and Rin-ichiro Taniguchi, Deep Learning-based Prediction Method for People Flows and Their Anomalies
, Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016, 2017.02.
|5.||Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile Marian Scuturici, Jean Marc Petit, Einoshin Suzuki, Skeleton clustering by multi-robot monitoring for fall risk discovery, Journal of Intelligent Information Systems, 10.1007/s10844-015-0392-1, 48, 1, 75-115, 2017.02, [URL], This paper tackles the problem of discovering subtle fall risks using skeleton clustering by multi-robot monitoring. We aim to identify whether a gait has fall risks and obtain useful information in inspecting fall risks. We employ clustering of walking postures and propose a similarity of two datasets with respect to the clusters. When a gait has fall risks, the similarity between the gait which is being observed and a normal gait which was monitored in advance exhibits a low value. In subtle fall risk discovery, unsafe skeletons, postures in which fall risks appear slightly as instabilities, are similar to safe skeletons and this fact causes the difficulty in clustering. To circumvent this difficulty, we propose two instability features, the horizontal deviation of the upper and lower bodies and the curvature of the back, which are sensitive to instabilities and a data preprocessing method which increases the ability to discriminate safe and unsafe skeletons. To evaluate our method, we prepare seven kinds of gait datasets of four persons. To identify whether a gait has fall risks, the first and second experiments use normal gait datasets of the same person and another person, respectively. The third experiments consider that how many skeletons are necessary to identify whether a gait has fall risks and then we inspect the obtained clusters. In clustering more than 500 skeletons, the combination of the proposed features and our preprocessing method discriminates gaits with fall risks and without fall risks and gathers unsafe skeletons into a few clusters..|
|6.||堀 磨伊也, 後藤 孝行, Shigeru Takano, Rin-ichiro Taniguchi, Power Demand Forecasting Using Meteorological Data and Human Congestion Information, IEEE International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA), 2016.10 , 2016.10.|
|7.||Maiya Hori, Takayuki Goto, Shigeru Takano, Rin-ichiro Taniguchi, Power Demand Forecasting Using Meteorological Data and Human Congestion Information, IEEE International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA), 2016.10.|
|8.||Hideki Sagara, Shigeru Takano, Yoshihiro OKADA, 3D Model Data Retrieval System Using KAZE Feature for Accepting 2D Image as Query,, VENOA 2016, IEEE CS Press, pp. 617-622, July 6-8, 2016., 2016.07.|
|9.||Yu Xiang, Shohei Nakamura, Hiroki Tamari, Shigeru Takano, Yoshihiro OKADA, 3D Model Generation of Cattle by Shape-from-Silhouette Method for ICT Agriculture, VENOA 2016, IEEE CS Press, pp. 611-616, July 6-8, 2016. , 2016.07.|
|10.||Hiromichi Abe, Takuya Kamizono, Kazuya Kinoshita, Kensuke Baba, Shigeru Takano, Kazuaki Murakami, Towards Activity Recognition of Learners in On-line Lecture, Journal of Mobile Multimedia, Vol. 11, No. 3&4. pp. 205-212, Nov. 2015. , 3&4, 205-212, 2015.11.|
|11.||Hiromichi Abe, Kazuya Kinoshita, Kensuke Baba, Shigeru Takano, Kazuaki Murakami, Analyzing Brain Waves for Activity Recognition of Learners, Proc. of the Third IFIP TC 5/8 International Conference, ICT-EurAsia 2015, and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015, Information and Communication Technology, LNCS Vol. 9357, pp. 64-73, 9537, 64-73, 2015.10.|
|12.||藤崎 清孝, 岡田 義広, 髙野 茂, 石田 浩二, キャンパス内デジタル放送システムの試作, 日本e-Learning学会論文誌, 15, 15-23, 2015.10.|
|13.||Satoshi Kuboi, Shigeru Takano, Kensuke Baba, Kazuaki Murakami, Approximate String Matching for Large-scale Event Processing
, The 4th Makassar International Conference on Electrical Engineering and Informatics (MICEEI 2014), 2014.11.
|14.||Daisuke Takayama, Yutaka Deguchi, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki, Multi-view Onboard Clustering of Skeleton Data for Fall Risk Discovery, Ambient Intelligence (AmI 2014), 258-273, 2014.11.|
|15.||Satoshi Kuboi, Shigeru Takano, Kensuke Baba, Kazuaki Murakami, An Evaluation of a Complex Event Processing Engine
, IIAI 3rd International Conference on Advanced Applied Informatics, 2014.09.
|16.||Takuya Kamizono, Hiromichi Abe, Shigeru Takano, Kensuke Baba, Kazuaki Murakami, Towards Activity Recognition of Learners by Kinect, IIAI 3rd International Conference on Advanced Applied Informatics, 2014.09.|
|17.||Einoshin Suzuki, Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Towards Facilitating the Development of a Monitoring System with Low-Cost Autonomous Mobile Robots, Information Search, Integration, and Personalization
, 421, 57-70, 2014.07.
|18.||Hiromichi Abe, Shigeru Takano, Kensuke Baba, Kazuaki Murakami, Towards Activity Recognition of Learners by Simple Electroencephalographs, Information Systems and Design of Communication, 2014.05.|
|19.||Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit, Einoshin Suzuki, Multiple-Robot Monitoring System Based on a Service-Oriented DBMS, Proc. Seventh ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2014), 2014.05.|
|20.||Ryo Sugimura, et. al., MOBILE GAME FOR LEARNING BACTERIOLOGY, Proc. IADIS 10th Int. Conf. on Mobile Learning 2014
, 5 pages, 2014.02.
|21.||Shigeru Takano, Ilya Loshchilov, David Meunier, Michele Sebag, Einoshin Suzuki, Fast Adaptive Object Detection towards a Smart Environment by Mobile Robots, Proc. Fourth International Joint Conference on Ambient Intelligence (AmI 2013), Volume 8309, 182-197, 2013.12.|
|22.||Shigeru Takano, Design of lifting wavelet filters for local feature analysis of an image, Proc. IIAI International Conference on Advanced Information Technologies, 2013.11.|
|23.||MISAKO MISHIMA, Takashi MATSUMOTO, Shigeru Takano, Osamu Matsuda, KOKOPIN App: A mobile platform for biogeography, MAED '13 Proceedings of the 2nd ACM International Workshop on Multimedia Ecological Data, in conjunction with ACM MULTIMEDIA 2013, 35-40, 2013.10.|
|24.||Shigeru Takano, Ilya Loshchilov, David Meunier, Michele Sebag, Einoshin Suzuki, Fast Adaptive Object Detection for a Mobile Robot, Proc. 2013 International Symposium on Information Science and Electrical Engineering (ISEE 2013), pp. 11, 2013.01.|
|25.||Kouhei Takemoto, Shigeru Takano, and Einoshin Suzuki, Human Detection by a Small Autonomous Mobile Robot, Extraction et Gestion des Connaissances (EGC'2012), pp. 531-536, Bordeaux, France, February 2012., 2012.02.|
|26.||Shigeru Takano and Einoshin Suzuki, New Object Detection for On-board Robot Vision by Lifting Complex Wavelet Transforms, Proc. Eleventh IEEE International Conference on Data Mining Workshops (ICDMW 2011), pp. 911-916, December 2011, IEEE Computer Society, 2011.12.|
|27.||Yoshihiro Okada and Shigeru Takano, Application Framework for Data Broadcast Contents Integrated with Web Services on Digital TV, Proc. of 15th Int. Conf. on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011), Andreas Konig, et al.(Eds.):KES 2010, LNAI 6884, pp. 63-72, 2011.09.|
|28.||Shigeru Takano, On-board Evolutionary Algorithm and Off-line Rule Discovery for Column Formation in Swarm Robotics, Proc. 2011 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2011), pp. 220-227
|29.||Shigeru Takano, Kensuke Baba,Sozo Inoue, Community Based Feature Database Construction for Mobile Image Retrieval, Proc. IADIS e-Society 2011 (ES 2011) Conference, pp. 580-582, 2011.03.|
|30.||Shingo Fukata, Shigeru Takano, Yoshihiro Okada and Kiyotaka Fujisaki, Speaker Identification System Using Lifting Wavelet Filters, Proc. IADIS e-Society 2011 (ES 2011) Conference, pp. 583-586, 2011.03.|
|31.||Nakamura, N., Takano, S. and Okada, Y., 3D Model Search Using Stochastic Attributed Relational Tree Matching, 17th International Multimedia Modeling Conference (MMM-2011) PartII, Lee, K.-T.; et al.(Eds.):Advances in Multimedia Modeling, LNCS 6524, pp. 348-358, 2011.01.|
|32.||Kumamoto, S., Takano, S. and Okada, Y., Context-based Multimedia Content Management Framework and Its Location-aware Web Applications, Proc. of 5th Int. Conf. on P2P, Parallel Grid, Cloud and Internet Computing (3PGCIC 2010), IEEE CS Press, pp. 98-104, 2010.11.|
|33.||Wakayama, Y., Okajima, S., Takano, S. and Okada, Y., IEC-Based Motion Retrieval System Using Laban Movement Analysis, Proc. Part I of 14th Int. Conf. on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010),
Rossitza Setchi, et al.(Eds.):KES 2010, LNAI 6279, pp. 251-260, 2010.09.
|34.||若山雄己, 高野茂, 岡田義広, 西野浩明, 対話型進化計算によるモーション作成・編集・検索システム, 第9回情報科学技術フォーラム(FIT2010), pp. 93-100, 2010.09.|
|35.||Asuki Kouno, Shigeru Takano, and Einoshin Suzuki, Constructing Low-cost Swarm Robots that March in Column Formation, Swarm Intelligence (ANTS), LNCS 6234, Springer-Verlag, pp. 556-557, 2010.09.|
|36.||F. Kawanobe, S. Takano and Y. Okada, Towards interactive image query system for contentbased image retrieval, Proc. of the 4th International Workshop on Semantic Media Adaptation and Personalization (SMAP09), pp. 56-61, 2009.12.|
|37.||E. Suzuki, S. Takano and H. Hirai, Toward Using Symbolic Discovery in Designing Controllers of Autonomous Swarm Robots, Proc. First International Workshop on LEarning and data Mining for Robotics (LEMIR), pp. 1-10, 2009.09.|
|38.||Y. Wakayama, S. Takano, Y. Okada and H. Nishino, Motion Generation System Using Interactive Evolutionary Computation and Signal Processing, Proc. of 2009 International Conference on Network-Based Information Systems (NBiS2009), pp. 492-498, 2009.08.|
|39.||H. Morimoto, F. Meing, S. Takano and Y. Okada, Style-sheets Extraction from Existing Digital Contents by Image Processing for Web-based BML Contents Management System, Proc. of 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization (CGIV2009), pp. 138-143, 2009.08.|
|40.||Nakamura, N., Takano, S. and Okada, Y. , 3D Multimedia Data Search System Based on Stochastic ARG Matching Method, Proc. in the 15th International Multimedia Modeling Conference (MMM 2009), 2009.01.|
|41.||Nakamura, N., Takano, S. and Okada, Y. , A Stochastic ARG Matching Based Video Scene Search System with a Sketch Query Interface, Proc. of the 5th Int. Conf. on Computer Graphics, Imaging and Visualization (CGIV08), IEEE CS Press, pp. 55-60, 2008.08.|
|42.||Shinsuke Ohtsuka, Satoshi Kawamoto, Shigeru Takano, Kensuke Baba, and Hiroto Yasuura, A Note on Biometrics-based Authentication with Portable Device, Proc. International Conference on Security and Cryptography (SECRYPT 2008), pp.99--102, INSTICC Press.
|43.||Hussain, M., Turghunjan Abdukirim, Takano, S. and Niijima, K.
, THE DYADIC LIFTING SCHEMES
AND THE DENOISING OF DIGITAL IMAGES, International Journal of Wavelets, Multiresolution
and Information Processing
, Vol. 6, No. 3 (2008) 331–351, 2008.06.
|44.||N. Nakamura, Y. Akazawa, S. Takano and Y. Okada, Virtual Space Construction Based on Contact Constraints Using Robot Vision Technology for 3D Graphics Applications, Proc. of 16th IEEE Int. Symp. on Robot & Human Interactive Communication, pp. 469-474, 2007.08.|
|45.||Yuka Higashijima, Shigeru Takano and Koichi Niijima, Lifting Wavelet Based Cognitive Vision System
, International Cognitive Vision Workshop - ICVW 2007, 2007, 2007.03.
|46.||Naoto Nakamura, Shigeru Takano, Yoshihiro Okada and Koichi Niijima, Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm, Proceedings of the 14th European Signal Processing Conference, CD-ROM, 2006.09.|
|47.||Yuka Higashijima, Shigeru Takano and Koichi Niijima, Face Recognition Using Long Haar-like Filters, Proceedings of the Image and Vision Computing New Zealand 2005(IVCNZ2005), pp. 43-48, 2005.11.|
|48.||Naoto Nakamura, Shigeru Takano and Koichi Niijima, Video scene retrieval based on the Layerization of images and the matching of layer-trees, Proceedings of the Image and Vision Computing New Zealand 2005(IVCNZ2005), pp. 449-454, 2005.11.|
|49.||S. Takano and K. Niijima, Person Identification Using Fast Face Learning of Lifting Dyadic Wavelet Filters, Proceedings of the 4th Internatinal Conference on Computer Recognition Systems (CORES'05), 815-823, pp. 893-900, 2005.05.|
|50.||Y. Akazawa, S. Takanao, Y. Okada and K. Niijima, Video based interface of AIBO for natural interactions, Proceedings of the 5th Annual European GAME-ON Conference (GAME-ON 2004) on Simulation and AI in Computer Games, 50-54, pp. 50-54, 2004.10.|
|51.||R. Ikeura, S. Takano and K. Niijima, Fast and accurate object tracking by successive learning of lifting wavelet filters, Proceedings of the 12th European Signal Processing Conference (EUSIPCO2004), pp.277-280, 2004.09.|
|52.||S. Takano, K. Niijima and K. Kuzume, Personal identification by multiresolution analysis of lifting dyadic wavelets, Proceedings of the 12th European Signal Processing Conference (EUSIPCO2004), pp.2283-2286, 2004.09.|
|53.||K. Kuzume, K. Niijima, and S. Takano, Design of a lifting wavelet processor for one dimensional signal detection, Proceedings of the 47th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS2004), vol.2, pp.421-424, 2004.07.|
|54.||K. Niijima, S. Takano and R. Ikeura, Fast image extraction by lifting wavelets, Proceedings of the 47th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS2004), 417-420, vol.2, pp.417-420, 2004.07.|
|55.||Shigeru Takano and Koichi Niijima, Extraction of subimage by lifting wavelet filters, IEICE Trans. Fundamentals, E83A, 8, 1559-1565, 2000.08.|
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