研究者詳細 - 森江　隆

論文 - 森江　隆

分割表示315 件中 81 - 100 件目／全件表示 >>

Restricted Boltzmann Machines without Random Number Generators for Efficient Digital Hardware Implementation 査読有り

S. Hori, T. Morie, H. Tamukoh

The 25th Int. Conf. on Artificial Neural Networks (ICANN) 9886 391 - 398 2016年09月

Depth-Based Visual Servoing Using Low-Accurate Arm 査読有り

L. Hofer, M. Tanaka, H. Tamukoh, A. A. F. Nassiraei, T. Morie

Joint 8th Int. Conf. on Soft Computing and Intelligent Systems and 17th Int. Symp. on Advanced Intelligent Systems (SCIS & ISIS 2016) 2016年08月

Analog Memory Characteristics of 1T1R MoOx Resistive Random Access Memory 査読有り

M. Jo, R. Katsumura, A. Tsurumaki-Fukuchi, M. Arita, Y. Takahashi, H. Ando, T. Morie

IEEE Silicon Nanoelectronics Workshop (SNW) 78 - 79 2016年06月

Human Detection and Face Recognition Using 3D Structure of Head and Face Surfaces Detected by RGB-D Sensor 査読有り

M. Tanaka, H. Matsubara, T. Morie

Journal of Robotics and Mechatronics 27 ( 6 ) 691 - 697 2015年12月

A Pixel-Parallel State-Propagation Algorithm with Self-Update of Propagation Direction for Subjective Contour Generation 査読有り

H. Sou, T. Morie

Int. Sympo. on Intelligent Signal Processing and Communication Systems (ISPACS 2015) 53 - 56 2015年11月

CMOS Circuits and Nanodevices for Spike Based Neural Computing 招待有り査読有り

T. Morie

IEEE Int. Meeting for Future of Electron Devices, Kansai 112 - 113 2015年06月

More than Mooreでブレークスルーを狙う脳型チップ

森江隆

日本神経回路学会誌 ( Japanese Neural Network Society ) 22 ( 2 ) 51 - 52 2015年01月

Morphological Associative Memory Employing a Split Store Method 査読有り

H. Tamukoh, K. Koga, H. Harada, T. Morie

Proc. 21th Int. Conf. on Neural Information Processing (ICONIP2014), (Lecture Notes in Computer Science, LNCS 8836) 3 341 - 348 2014年10月

A Chaotic Spiking Oscillator That Acts As a Filter of Spike Trains 査読有り

S. Uenohara, T. Morie

Abs. Collection of the 2014 Int. Symp. on Nonlinear Theory and its Applications (NOLTA2014) 723 - 726 2014年09月

A Motion Detection Model Inspired by Hippocampal Function and Its Applications to Obstacle Detection 査読有り

H. Liang, T. Morie

Neurocomputing 129 59 - 66 2014年04月

M-HOG 特徴量と色相の共起情報を用いた人検出法査読有り

中島佑樹, タンジュークイ, 金亨燮, 森江隆, 石川聖二

バイオメディカル・ファジィ・システム学会誌 16 ( 1 ) 67 - 74 2014年04月

Digital Circuit Design of Pulse-coupled Phase Oscillator Systems for Coupled MRF Models 査読有り

Y. Suedomi, H. Tamukoh, M. Tanaka, K. Matsuzaka, T. Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 89 - 92 2014年03月

Object Segmentation Using 3D Information Obtained from RGB-D Sensor 査読有り

T. Matsumoto, M. Tanaka, H. Tamukoh, T. Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 377 - 380 2014年03月

Sound Quality Improvement of Low Bit Rate Coded Audio Signals with Nonlinear Functions 査読有り

T. Yoshino, Y. Sato, H. Tamukoh, T. Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 237 - 240 2014年03月

Robustness to CMOS Device Mismatches of Time-domain Voltage/Current Sampling Circuits for Implementing Large-scale Coupled Nonlinear Dynamical Systems 査読有り

S. Uenohara, D. Atuti, K. Matsuzaka, H. Tamukoh, T. Morie

2014 RISP Int. Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2014) 93 - 96 2014年03月

Videotaped obstacle extraction from a moving camera 査読有り

Qian S., Tan J., Kim H., Ishikawa S., Morie T., Shinomiya T.

International Journal of Innovative Computing, Information and Control 10 ( 2 ) 717 - 728 2014年02月

記述言語：英語掲載種別：研究論文（学術雑誌）

In automatic collision avoidance systems, the ability to detect obstacles is important. This paper proposes a method of automatic obstacles detection employing a camera mounted on a vehicle. Although various methods of obstacles detection have already been reported, they normally detect moving objects such as pedestrians and bicycles. In this paper, a method is proposed for detecting obstacles on a road, even if they are moving or static, by the use of background modeling and road region classification. Background modeling is often used to detect moving objects when a camera is static. In this paper, we apply it to a moving camera case in order to obtain foreground images. Then we calculate the camera motion parameters using the correspondence of feature points between two consecutive images and detect the road region using motion compensation. In this road region, we carry out regional classification. We can delete all objects which are not obstacles in the foreground images based on the result of the regional classification. In the performed experiments, it is shown that the proposed method is able to extract the shape of both static and moving obstacles in a frontal view from a car. © 2014 ISSN 1349-4198.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84893413251&origin=inward

Classifying 2D and 3D objects on a road employing the road plane 査読有り

Qian S., Tan J., Kim H., Ishikawa S., Morie T., Shinomiya T.

Proceedings of the SICE Annual Conference 1689 - 1692 2013年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Obstacles detection is an important technology in autonomous collision avoidance systems. We have already proposed an obstacle detection method based on background modeling. But this method detects 2D and 3D objects simultaneously. Since these 2D objects are not dangerous to driving, they will reduce the accuracy of detection if they are detected as obstacles. In order not to detect these 2D objects, this paper proposes a method for classifying 2D objects and 3D objects. The proposed method first estimates the camera motion parameters from the correspondences of feature points between two successive images. We calculate the 3D positions of the feature points on a detected object in the world coordinate system using triangulation. Then we estimate the parameters of the road plane using 3D positions of those feature points. Finally we calculate the distances from the 3D positions of the feature points to the road plane. Based on these distances, we can classify 2D objects and 3D objects. Experimental results show satisfactory performance of the proposed method.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84888611260&origin=inward

Parameterized Digital Hardware Design of Pulse-coupled Phase Oscillator Model toward Spike-based Computing 招待有り査読有り

Y. Suedomi, H. Tamukoh, M. Tanaka, K. Matsuzaka, T. Morie

Proc. 20th Int. Conf. on Neural Information Processing (ICONIP2013) 2013年11月

A PWM-Mode CMOS Threshold-Coupled-Map Circuit Robust to Device Mismatches 査読有り

S. Uenohara, D. Atuti, K. Matsuzaka, H. Tamukoh, T. Morie, K. Aihara

Abs. Collection of the 2013 Int. Symp. on Nonlinear Theory and Its Applications (NOLTA2013) 503 - 506 2013年09月

Road region estimation and obstacles extraction using a monocular camera 査読有り

Qian S., Tan J., Kim H., Ishikawa S., Morie T., Shinomiya T.

International Journal of Innovative Computing, Information and Control 9 ( 9 ) 3561 - 3572 2013年07月

記述言語：英語掲載種別：研究論文（学術雑誌）

An obstacles detection method is proposed which employs a camera mounted on a vehicle. Although various methods of obstacles detection have already been reported, they normally detect only moving obstacles and not static obstacles, and a detected obstacle is represented by a rectangular frame that surrounds this obstacle, which does not provide shape of the obstacle. In this paper, a method is proposed for detecting obstacles on a road, irrespective of moving or static, by use of background modeling and road region detection. The output of the proposed method is the shape of obstacles. Background modeling is often used to detect moving objects when a camera is static. In this paper, we apply it to a moving camera case in order to obtain foreground images. Then we extract the road region using SVM. In this road region, we carry out region classification. We can delete all the objects which are not obstacles in the foreground images based on the result of the region classification. In the performed experiments, it is shown that the proposed method is able to extract the shape of both static and moving obstacles when a car is driving. © 2013 ISSN 1349-4198.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84880054018&origin=inward

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