Faculty Profiles - OGAWARA Koichi

Published Papers - OGAWARA Koichi

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Improvements over Coordinate Regression Approach for Large-Scale Face Alignment

Haoqi Gao, Koichi Ogawara （Part： Last author,　Corresponding author )

IIEEJ Transactions on Image Electronics and Visual Computing 10 ( 1 ) 127 - 135 2022.06 [Refereed]

Face Reconstruction Algorithm based on Lightweight Convolutional Neural Networks and Channel-wise Attention

Haoqi Gao, Koichi Ogawara （Part： Last author,　Corresponding author )

IIEEJ Transactions on Image Electronics and Visual Computing 10 ( 1 ) 90 - 97 2022.06 [Refereed]

Segmentation and Shape Estimation of Multiple Deformed Cloths Using a CNN-Based Landmark Detector and Clustering," 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 7043-7049, doi: 10.1109/ICRA46639.2022.9811551

D. Sonegawa, K. Ogawara （Part： Last author,　Corresponding author )

2022 International Conference on Robotics and Automation 7043 - 7049 2022.05 [Refereed]

Face Alignment Using a GAN-based Photorealistic Synthetic Dataset

Haoqi Gao, Koichi Ogawara （Part： Last author )

2022 7th International Conference on Control and Robotics Engineering (ICCRE) ( IEEE ) 2022.04 [Refereed]

DOI

Face alignment by learning from small real datasets and large synthetic datasets

aoqi Gao, Koichi Ogawara （Part： Last author )

2022 Asia Conference on Cloud Computing, Computer Vision and Image Processing 2022.03 [Refereed]

Bidirectional Mapping Augmentation Algorithm for Synthetic Images Based on Generative Adversarial Network

Haoqi Gao, Koichi Ogawara （Part： Last author )

IIEEJ Transactions on Image Electronics and Visual Computing 8 ( 2 ) 110 - 120 2020.12 [Refereed]

Estimation of object class and orientation from multiple viewpoints and relative camera orientation constraints

Koichi Ogawara, Keita Iseki （Part： Lead author,　Corresponding author )

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ( IEEE ) 10588 - 10594 2020.10 [Refereed]

DOI

Generative adversarial network for bidirectional mappings between synthetic and real facial image

HAOQi GAO, Koichi Ogawara （Part： Last author )

Twelfth International Conference on Digital Image Processing (ICDIP 2020) ( SPIE ) 136 - 145 2020.06 [Refereed]

DOI

CGAN-based Synthetic Medical Image Augmentation between Retinal Fundus Images and Vessel Segmented Images

Gao HaoQi, Koichi Ogawara （Part： Last author )

2020 5th International Conference on Control and Robotics Engineering (ICCRE) ( IEEE ) 218 - 223 2020.04 [Refereed]

DOI

Seminar Report: the 105th Robotics Seminar

Ogawara Koichi

Journal of the Robotics Society of Japan ( The Robotics Society of Japan ) 35 ( 7 ) 536 - 537 2017

DOI

Development of Power Assist Suit for Supporting and Transporting Heavy Object with Single Motor

Tomoya Takeno, Ayano Murakami, Han Peng, Koichi Ogawara, Arata Suzuki, Kunitomo Kikuchi

2015 54TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) ( IEEE ) 1240 - 1243 2015 [Refereed]

Identification of people walking along curved trajectories

Yumi Iwashita, Koichi Ogawara, Ryo Kurazume

PATTERN RECOGNITION LETTERS ( ELSEVIER SCIENCE BV ) 48 60 - 69 2014.10 [Refereed]

Conventional methods of gait analysis for person identification use features extracted from a sequence of camera images taken during one or more gait cycles. The walking direction is implicitly assumed not to change. However, with the exception of very particular cases, such as walking on a circle centered on the camera, or along a line passing through the camera, there is always some degree of orientation change, most pronounced when the person is closer to the camera. This change in the angle between the velocity vector and the position vector in respect to the camera causes a decrease in performance for conventional methods. To address this issue we propose in this paper a new method, which provides improved identification in this context of orientation change. The proposed method uses a 4D gait database consisting of multiple 3D shape models of walking people and adaptive virtual image synthesis with high accuracy. Each frame, for the duration of a gait cycle, is used to estimate the walking direction of the subject, and a virtual image corresponding to the estimated direction is synthesized from the 4D gait database. The identification uses affine moment invariants as gait features. The efficiency of the proposed method is demonstrated through experiments using a database that includes 42 subjects. (C) 2014 Elsevier B. V. All rights reserved.

DOI

顔の部位識別に基づくマーカレスモーションキャプチャに関する研究

赤木康宏, 古川亮, 佐川立昌, 小川原光一, 川崎洋

精密工学会論文誌 79 ( 11 ) 1152 - 1158 2013.11 [Refereed]

A Voting-Based Sequential Pattern Recognition Method

Koichi Ogawara, Masahiro Fukutomi, Seiichi Uchida, Yaokai Feng （Part： Lead author )

PLOS ONE ( PUBLIC LIBRARY SCIENCE ) 8 ( 10 ) 2013.10 [Refereed]

We propose a novel method for recognizing sequential patterns such as motion trajectory of biological objects (i.e., cells, organelle, protein molecules, etc.), human behavior motion, and meteorological data. In the proposed method, a local classifier is prepared for every point (or timing or frame) and then the whole pattern is recognized by majority voting of the recognition results of the local classifiers. The voting strategy has a strong benefit that even if an input pattern has a very large deviation from a prototype locally at several points, they do not severely influence the recognition result; they are treated just as several incorrect votes and thus will be neglected successfully through the majority voting. For regularizing the recognition result, we introduce partial-dependency to local classifiers. An important point is that this dependency is introduced to not only local classifiers at neighboring point pairs but also to those at distant point pairs. Although, the dependency makes the problem non-Markovian (i.e., higher-order Markovian), it can still be solved efficiently by using a graph cut algorithm with polynomial-order computations. The experimental results revealed that the proposed method can achieve better recognition accuracy while utilizing the above characteristics of the proposed method.

DOI

Marker-less Facial Motion Capture based on the Parts Recognition.

Yasuhiro Akagi, Ryo Furukawa, Ryusuke Sagawa, Koichi Ogawara, Hiroshi Kawasaki

Journal of WSCG 21 ( 2 ) 137 - 144 2013 [Refereed]

Expanding gait identification methods from straight to curved trajectories

Yumi Iwashita, Ryo Kurazume, Koichi Ogawara

2013 IEEE WORKSHOP ON APPLICATIONS OF COMPUTER VISION (WACV) ( IEEE ) 193 - 199 2013 [Refereed]

Conventional methods of gait analysis for person identification use features extracted from a sequence of camera images taken during one or more gait cycles. An implicit assumption is made that the walking direction does not change. However, cameras deployed in real-world environments (and often placed at corners) capture images of humans who walk on paths that, for a variety of reasons, such as turning corners or avoiding obstacles, are not straight but curved. This change of the direction of the velocity vector causes a decrease in performance for conventional methods. In this paper we address this aspect, and propose a method that offers improved identification results for people walking on curved trajectories. The large diversity of curved trajectories makes the collection of complete real world data infeasible. The proposed method utilizes a 4D gait database consisting of multiple 3D shape models of walking subjects and adaptive virtual image synthesis. Each frame, for the duration of a gait cycle, is used to estimate a walking direction for the subject, and consequently a virtual image corresponding to this estimated direction is synthesized from the 4D gait database. The identification uses affine moment invariants as gait features. Experiments using the 4D gait database of 21 subjects show that the proposed method has a higher recognition performance than conventional methods.

A facial motion tracking and transfer method based on a key point detection

Yasuhiro Akagi, Ryo Furukawa, Ryusuke Sagawa, Koichi Ogawara, Hiroshi Kawasaki

WSCG 2013, COMMUNICATION PAPERS PROCEEDINGS ( UNION AGENCY SCIENCE PRESS ) 137 - 144 2013 [Refereed]

Facial animation is one of the most important contents in 3D CG animations. By the development of scanning and tracking methods of a facial motion, a face model which consists of more than 100,000 points can be used for the animations. To edit the facial animations, key point based approaches such as "face rigging" are still useful ways. Even if a facial tracking method gives us all point-to-point correspondences, a detection method of a suitable set of key points is needed for content creators. Then, we propose a method to detect the key points which efficiently represent motions of a face. We optimize the key points for a Radial Basis Function (RBF) based 3D deformation technique. The RBF based deformation is a common technique to represent a movement of 3D objects in CG animations. Since the key point based approaches usually deform objects by interpolating movements of the key points, these approaches cause errors between the deformed shapes and the original ones. To minimize the errors, we propose a method which automatically inserts additional key points by detecting the area where the error is larger than the surrounding area. Finally, by utilizing the suitable set of key points, the proposed method creates a motion of a face which are transferred form another motion of a face.

Non-Markovian Dynamic Time Warping

Seiichi Uchida, Masahiro Fukutomi, Koichi Ogawara, Yaokai Feng

2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) ( IEEE ) 2294 - 2297 2012 [Refereed]

This paper proposes a new dynamic time warping (DTW) method, called non-Markovian DTW In the conventional DTW, the warping function is optimized generally by dynamic programming (DP) subject to some Markovian constraints which restrict the relationship between neighboring time points. In contrast, the non-Markovian DTW can introduce non-Markovian constraints for dealing with the relationship between points with a large time interval. This new and promising ability of DTW is realized by using graph cut as the optimizer of the warping function instead of DP. Specifically, the conventional DTW problem is first converted as an equivalent minimum cut problem on a graph and then edges representing the non-Markovian constraints are added to the graph. An experiment on online character recognition showed the advantage of using non-Markovian constraints during DTW.

Approximate Belief Propagation by Hierarchical Averaging of Outgoing Messages

OGAWARA Koichi （Part： Lead author )

20th International Conference on Pattern Recognition(ICPR) ( The Institute of Electronics, Information and Communication Engineers ) J94-D ( 3 ) 593 - 603 2011.03 [Refereed]

本論文では,ノードの出力メッセージをそのノードの全出力メッセージの平均値で置き換え,更に低解像度グラフから元のグラフへと階層的にメッセージを伝搬することにより,画像に対して適用した場合に通常の確率伝搬法と比較して計算時間を約2分の1から3分の1に,メモリ使用量を40%に削減可能な近似確率伝搬法を提案する.また,提案手法をCPU及びGPU上に実装し,Middleburyステレオデータセットを使用して通常の確率伝搬法と比較実験を行い,わずかな精度の低下と引換えに計算時間とメモリ使用量の両面において性能が大きく改善されたことを示す.

DOI

Detecting Frequent Patterns in Time Series Data using Partly Locality Sensitive Hashing

OGAWARA Koichi, TANABE Yasufumi, KURAZUME Ryo, HASEGAWA Tsutomu （Part： Lead author )

Journal of the Robotics Society of Japan ( The Robotics Society of Japan ) 29 ( 1 ) 67 - 76 2011.01 [Refereed]

Frequent patterns in time series data are useful clues to learn previously unknown events in an unsupervised way. In this paper, we propose a method for detecting frequent patterns in long time series data efficiently. The major contribution of the paper is two-fold: (1) Partly Locality Sensitive Hashing (PLSH) is proposed to find frequent patterns efficiently and (2) the problem of finding consecutive time frames that have a large number of frequent patterns is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time O (N 1+1/α) thanks to PLSH where N is the total amount of data. The proposed method was evaluated by detecting frequent whole body motions in a video sequence as well as by detecting frequent everyday manipulation tasks in motion capture data.

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Published Papers - OGAWARA Koichi