Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Hideaki Uchiyama Last modified date:2020.06.29

Associate Professor / Library


Presentations
1. Shunsuke Sakurai, Hideaki Uchiyama, Atshushi Shimada, Rin ichiro Taniguchi, Plant growth prediction using convolutional LSTM, 14th International Conference on Computer Vision Theory and Applications, VISAPP 2019 - Part of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, 2019.01, This paper presents a method for predicting plant growth in future images from past images, as a new phenotyping technology. This is achieved by modeling the representation of plant growth based on neural network. In order to learn the long-term dependencies in plant growth from the images, we propose to employ a Convolutional LSTM based framework. Especially, We apply an encoder-decoder model inspired by a framework on future frame prediction to model the representation of plant growth effectively. In addition, we propose two additional loss terms to put the constraints on shape changes of leaves between consecutive images. In the evaluation, we demonstrated the effectiveness of the proposed loss functions through the comparisons using labeled plant growth images..
2. Chuanhua Lu, Hideaki Uchiyama, Diego Thomas, Atsushi Shimada, Rin ichiro Taniguchi, Multi-pedestrian tracking system based on asynchronized IMUs, Short Paper of the 10th International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, IPIN-WiP 2019, 2019.01, We propose a multi-pedestrian tracking system based on MEMS based IMUs as a novel tool for human behavior analysis. With asynchronized multiple IMUs, our system can track IMU-attached pedestrians in synchronization at a high frame rate in the large environment, compared with vision based approaches. The output data is similar to standard PDR systems as follows: the time-series position, velocity, and heading of the pedestrians in the 3D space. To realize our system, we propose a simple but effective calibration technique for synchronizing the timelines of the asynchronized IMUs. With our system, users can analyze the detailed motion behaviors of the people who participate in a group work or a collective activity, quantitatively. By combining with other sensors such as an eye tracker, our system can further provide more comprehensive data in the experiments..
3. Keiya Maekawa, Yoichi Tomiura, Satoshi Fukuda, Emi Ishita, Hideaki Uchiyama, Improving OCR for Historical Documents by Modeling Image Distortion, 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019, 2019.01, Archives hold printed historical documents, many of which have deteriorated. It is difficult to extract text from such images without errors using optical character recognition (OCR). This problem reduces the accuracy of information retrieval. Therefore, it is necessary to improve the performance of OCR for images of deteriorated documents. One approach is to convert images of deteriorated documents to clear images, to make it easier for an OCR system to recognize text. To perform this conversion using a neural network, data is needed to train it. It is hard to prepare training data consisting of pairs of a deteriorated image and an image from which deterioration has been removed; however, it is easy to prepare training data consisting of pairs of a clear image and an image created by adding noise to it. In this study, PDFs of historical documents were collected and converted to text and JPEG images. Noise was added to the JPEG images to create a dataset in which the images had noise similar to that of the actual printed documents. U-Net, a type of neural network, was trained using this dataset. The performance of OCR for an image with noise in the test data was compared with the performance of OCR for an image generated from it by the trained U-Net. An improvement in the OCR recognition rate was confirmed..
4. So Tashiro, Hideaki Uchiyama, Diego Thomas, Rin Ichiro Taniguchi, 3D positioning system based on one-handed thumb interactions for 3d annotation placement, 26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019, 2019.03, This paper presents a 3D positioning system based on one-handed thumb interactions for simple 3D annotation placement with a smart-phone. To place an annotation at a target point in the real environment, the 3D coordinate of the point is computed by interactively selecting the corresponding points in multiple views by users while performing SLAM. Generally, it is difficult for users to precisely select an intended pixel on the touchscreen. Therefore, we propose to compute the 3D coordinate from multiple observations with a robust estimator to have the tolerance to the inaccurate user inputs. In addition, we developed three pixel selection methods based on one-handed thumb interactions. A pixel is selected at the thumb position at a live view in FingAR, the position of a reticle marker at a live view in SnipAR, or that of a movable reticle marker at a freezed view in FreezAR. In the preliminary evaluation, we investigated the 3D positioning accuracy of each method..
5. Remy Maxence, Hideaki Uchiyama, Hiroshi Kawasaki, Diego Thomas, Vincent Nozick, Hideo Saito, Mobile Photometric Stereo with Keypoint-Based SLAM for Dense 3D Reconstruction, 7th International Conference on 3D Vision, 3DV 2019, 2019.09, The standard photometric stereo is a technique to densely reconstruct objects' surfaces using light variation under the assumption of a static camera with a moving light source. In this work, we use photometric stereo to reconstruct dense 3D scenes while moving the camera and the light altogether. In such non-static case, camera poses as well as correspondences between pixels of each frame to apply photometric stereo are required. ORB-SLAM is a technique that can be used to estimate camera poses. To retrieve correspondences, our idea is to start from a sparse 3D mesh obtained with ORB SLAM and then densify the mesh by a plane sweep method using a multi-view photometric consistency. By combining ORB-SLAM and photometric stereo, it is possible to reconstruct dense 3D scenes with a off-the-shelf smartphone and its embedded torchlight. Note that SLAM systems usually struggle with textureless object, which is effectively compensated by the photometric stereo in our method. Experiments are conducted to show that our proposed method gives better results than SLAM alone or COLMAP, especially for partially textureless surfaces..
6. Hayato Onizuka, DIego Thomas, Hideaki Uchiyama, Rin Ichiro Taniguchi, Landmark-guided deformation transfer of template facial expressions for automatic generation of avatar blendshapes, 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019, 2019.10, Blendshape models are commonly used to track and re-target facial expressions to virtual avatars using RGB-D cameras and without using any facial marker. When using blendshape models, the target avatar model must possess a set of key-shapes that can be blended depending on the estimated facial expression. Creating realistic set of key-shapes is extremely difficult and requires time and professional expertise. As a consequence, blendshape-based re-targeting technology can only be used with a limited amount of pre-built avatar models, which is not attractive for the large public. In this paper, we propose an automatic method to easily generate realistic key-shapes of any avatar that map directly to the source blendshape model (the user is only required to select a few facial landmarks on the avatar mesh). By doing so, captured facial motion can be easily re-targeted to any avatar, even when the avatar has largely different shape and topology compared with the source template mesh. Our experimental results show the accuracy of our proposed method compared with the state-of-the-art method for mesh deformation transfer..
7. Ami Miyake, Hideaki Uchiyama, Atsushi Shimada, Rin Ichiro Taniguchi, Planar
Accurate and stable 3D positioning system via interactive plane reconstruction for handheld augmented reality, 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, 2020.01, This paper presents a ray-casting-based three-dimensional (3D) positioning system that interactively reconstructs scene structures for handheld augmented reality. The proposed system employs visual simultaneous localization and mapping (vSLAM) technology to acquire camera poses of a smartphone and sparse 3D feature points in an unknown scene. First, users specify a geometric shape region, such as a plane, in captured images while capturing a scene. This is performed by manually selecting some of the feature points generated by vSLAM in the region. Next, the system computes the shape parameter with the selected feature points so that the scene structure is reconstructed densely. Subsequently, users select the pixel of a target point in the scene at one camera view for 3D positioning. Finally, the system computes the intersection between the 3D ray computed with the selected pixel and the reconstructed scene structure to determine the 3D coordinates of the target point. Owing to the proposed interactive reconstruction, the scene structure can be estimated accurately and stably; therefore, 3D positioning will be accurate. Because the geometric shape used for the scene structure is a plane in this study, our system is referred to as PlanAR. In the evaluation, the performance of our system is compared statistically with an existing 3D positioning system to demonstrate the accuracy and stability of our system..
8. Kazuki Nishiguchi, Walid Bousselham, Hideaki Uchiyama, Diego Thomas, Atsushi Shimada, Rin Ichiro Taniguchi, Generating a consistent global map under intermittent mapping conditions for large-scale vision-based navigation, 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, 2020.01, Localization is the process to compute sensor poses based on vision technologies such as visual Simultaneous Localization And Mapping (vSLAM). It can generally be applied to navigation systems . To achieve this, a global map is essential such that the relocalization process requires a single consistent map represented with an unified coordinate system. However, a large-scale global map cannot be created at once due to insufficient visual features at some moments. This paper presents an interactive method to generate a consistent global map from intermittent maps created by vSLAM independently via global reference points. First, vSLAM is applied to individual image sequences to create maps independently. At the same time, multiple reference points with known latitude and longitude are interactively recorded in each map. Then, the coordinate system of each individual map is converted into the one that has metric scale and unified axes with the reference points. Finally, the individual maps are merged into a single map based on the relative position of each origin. In the evaluation, we show the result of map merging and relocalization with our dataset to confirm the effectiveness of our method for navigation tasks. In addition, the report on participating in the navigation competition in a practical environment is also discussed..
9. Tomohiro Hamamoto, Hideaki Uchiyama, Atsushi Shimada, Rin Ichiro Taniguchi, 3D plant growth prediction via image-to-image translation, 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020, 2020.01, This paper presents a method to predict three-dimensional (3D) plant growth with RGB-D images. Based on neural network based image translation and time-series prediction, we construct a system that gives the predicted result of RGB-D images from several past RGB-D images. Since both RGB and depth images are incorporated into our system, the plant growth can be represented in 3D space. In the evaluation, the performance of our proposed network is investigated by focusing on clarifying the importance of each module in the network. We have verified how the prediction accuracy changes depending on the internal structure of the our network..
10. Hideaki Uchiyama, Yuji Oyamada, Transparent Random Dot Markers, International Conference on Pattern Recognition, 2018.08.
11. Nicolas Olivier, Hideaki Uchiyama, Masashi Mishima, Diego Thomas, Rin-ichiro Taniguchi, Rafael Roberto, Joao Paulo Silva do Monte Lima, Veronica Teichrieb, Live Structural Modeling Using RGB-D SLAM, International Conference on Robotics and Automation, 2018.05.
12. Rafael Roberto, Joao Paulo Lima, Hideaki Uchiyama, Clemens Arth, Veronica Teichrieb, Rin-Ichiro Taniguchi, Dieter Schmalstieg, Incremental Structural Modeling Based on Geometric and Statistical Analyses, IEEE Winter Conference on Applications of Computer Vision, 2018.03.
13. Hideaki Uchiyama, Shunsuke Sakurai, Yoshiki Hashimoto, Atsutoshi Hanasaki, Daisaku Arita, Takashi Okayasu, Atsushi Shimada, Rin-ichiro Taniguchi, Sensing technologies for advanced smart agricultural systems, International Conference on Sensing Technology, 2017.12.
14. Ryoshuke Ichikari, Takeshi Kurata, Koji Makita, Takafumi Taketomi, Hideaki Uchiyama, Tomotsugu Kondo, Shohei Mori and Fumihisa Shibata, Reference framework on vSRT-method benchmarking for MAR, International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments, 2017.11.
15. Hideaki Uchiyama, Shunsuke Sakurai, Masashi Mishima, Daisaku Arita, Takashi Okayasu, Atsushi Shimada, Rin-ichiro Taniguchi, An easy-to-setup 3D phenotyping platform for KOMATSUNA dataset, Computer Vision Problems in Plant Phenotyping, 2017.10.
16. Tsubasa Minematsu, Atsushi Shimada, Hideaki Uchiyama, Rin-Ichiro Taniguchi, Simple Combination of Appearance and Depth for Foreground Segmentation, Background learning for detection and tracking from RGBD videos, 2017.09.
17. Chuanhua Lu, Hideaki Uchiyama, Diego Thomas, Rin-ichiro Taniguchi, A PDR System using IMU based Gait Tracking and Map Matching, International Conference on Indoor Positioning and Indoor Navigation, 2017.09, 歩行者の測位技術において,IMUを用いた歩行軌跡算出に対して対象空間の地図による制約を融合し,高精度な測位技術を提案した..
18. Chao Ma, Ngo Thanh Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi, Mixed features for face detection in thermal image, International Conference on Quality Control by Artificial Vision, 2017.05.
19. Hideaki Uchiyama, Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, 3D surveillance system using camera array, The 11th International Workshop on Information Search, Integration, and Personalization, 2017.11.
20. Hideaki Uchiyama, Tracking competitions for evaluating camera based sensor pose estimation, 5th Asian Workshop on Smart Sensor Systems, 2017.03.
21. Atsutoshi Hanasaki, Hideaki Uchiyama, Atsushi Shimada, Rin-ichiro Taniguchi, Spatial People Localization on Floor Map using Multiple Fisheye Cameras, The International Workshop on Frontiers of Computer Vision , 2017.02.
22. 馬 超, NGO THANH TRUNG, 内山 英昭, 長原 一, 島田 敬士, 谷口 倫一郎, Mixed Feature for Face Detection in Thermal Image, 情報処理学会研究報告, 2016.11.
23. Naoyuki Maeda, Amandine Paulo-Guieu, THANH TRUNG NGO, Hideaki Uchiyama, Hajime Nagahara, Rin-ichiro Taniguchi, Structure from Motion for Unordered Fisheye Images, The 12th Joint Workshop on Machine Perception and Robotics, 2016.11.
24. Chao Ma, THANH TRUNG NGO, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi, Mixed Feature for Face Detection in Thermal Image, The 12th Joint Workshop on Machine Perception and Robotics, 2016.11.
25. Liming Yang, Hideaki Uchiyama, Jean-Marie Normand, Guillaume Moreau, Hajime Nagahara, Rin-ichiro Taniguchi, Real-time surface of revolution reconstruction on dense SLAM, International Conference on 3D Vision, 2016.10.
26. Hideaki Uchiyama, Tracking Competitions for evaluating visual SLAM techniques, ECCV Workshop on Datasets and Performance Analysis in Early Vision, 2016.10.
27. Mohamed A. Abdelwahab, Moataz M. Abdelwahab, Hideaki Uchiyama, Atsushi Shimada, Rin-ichiro Taniguchi, Video Object Segmentation based on Superpixel Trajectories, International Conference on Image Analysis and Recognition, 2016.07.
28. Yoshiki Hashimoto, Daisaku Arita, Atsushi Shimada, Takashi OKAYASU, Hideaki Uchiyama, Rin-ichiro Taniguchi, Farmer position estimation in a tomato plant green house with smart devices, International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering, 2016.06.
29. Yoshiki Hashimoto, Daisaku Arita, Atsushi Shimada, Takashi Yoshinaga, Takashi OKAYASU, Hideaki Uchiyama, Rin-ichiro Taniguchi, Measurement and Visualization of Farm Work Information, International Conference on Agriculture Engineering , 2016.06.
30. Ryo Kawahata, Atsushi Shimada, Takayoshi Yamashita, Hideaki Uchiyama, Rin-ichiro Taniguchi, Design of a Low-false-positive Gesture for a Wearable Device, 5th International Conference on Pattern Recognition Applications and Methods, 2016.02.
31. Mohamed Abdelwahab, Tsubasa Minematsu, Hideaki Uchiyama, Atsushi Shimada, Moataz Abdelwahab, Rin-ichiro Taniguchi, Object Video Segmentation Using Superpixel Motion, 22th Japan-Korea Joint Workshop on Frontiers of Computer Vision, 2016.02.
32. Naoyuki Maeda, Amandine Paulo-Guieu, THANH TRUNG NGO, Hideaki Uchiyama, Hajime Nagahara, Rin-ichiro Taniguchi, Bundler for Fisheye Camera Models, 22th Japan-Korea Joint Workshop on Frontiers of Computer Vision, 2016.02.
33. Yosuke Nakagawa, Hideaki Uchiyama, Hajime Nagahara, Rin-ichiro Taniguchi, Estimating surface normals with depth image gradients for fast and accurate registration, International Conference on 3D Vision, 2015.10.
34. Tsubasa Minematsu, Hideaki Uchiyama, Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, Adaptive search of background models for object detection in images taken by moving cameras, International Conference on Image Processing, 2015.09.
35. Hideaki Uchiyama, Shinichiro Haruyama, Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, Spatially-Multiplexed MIMO Markers, IEEE 10th Symposium on 3D User Interfaces, 2015.03.
36. Hao Liu, Xing Xu, Hideaki Uchiyama, Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, Query expansion with pairwise learning in object retrieval challenge, 21th Japan-Korea Joint Workshop on Frontiers of Computer Vision, 2015.01.
37. Tsubasa Minematsu, Hideaki Uchiyama, Atsushi Shimada, Hajime Nagahara, Rin-ichiro Taniguchi, Evaluation of foreground detection methodology for a moving camera, 21th Japan-Korea Joint Workshop on Frontiers of Computer Vision, 2015.01.