研究者詳細 - 森江　隆

論文 - 森江　隆

分割表示315 件中 101 - 120 件目／全件表示 >>

Experimental Distinction between Chaotic and Strange Nonchaotic Attractors on the Basis of Consistency 査読有り

Uenohara, T. Mitsui, Y. Hirata, T. Morie, Y. Horio, K. Aihara

Chaos 23 ( 2 ) 23110-1 - 23110-9 2013年05月

Obstacles extraction using a moving camera 査読有り

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7729 LNCS ( PART 2 ) 441 - 453 2013年04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）

A method of automatic obstacles detection is proposed which employs 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, irrespective of moving or static, by the employment of the background modeling and the road region classification. The background modeling is often used to detect moving objects when a camera is static. In this paper, we apply it to the moving camera case to get foreground images. Then we extract the road region using SVM. In this road region, we carry out region classification. Then we can delete all the things which are not obstacles in the foreground images using the result of the region classification. In the performed experiments, it is shown that the proposed method is able to extract the shapes of both static and moving obstacles in a frontal view from a car. © 2013 Springer-Verlag.

DOI： 10.1007/978-3-642-37484-5_36

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84876011141&origin=inward

Gabor Features for Real-Time Road Environment Classification 査読有り

L. Mioulet, T. P. Breckon, A. Mouton, H. Liang, T. Morie

Proc. IEEE Int. Conf. on Industrial Technology (ICIT) 1117 - 1121 2013年02月

スパイクベース非線形演算のためのCMOS結合位相振動子回路

東原敬, 松坂建治, 西広海, 森江, 隆

電子情報通信学会 NLP研究会 112 ( 389 ) 137 - 142 2013年01月

FPGAにより制御される専用アナログチップを用いたスパイキングニューラルネットワークシステムの開発

前田道孝, Frank Maldonado H., 松尾貴之, 田中秀樹, 梁海超, 松坂建治, 森江隆, 合原一幸

電子情報通信学会 NC研究会 112 ( 390 ) 181 - 186 2013年01月

電圧・電流波形サンプリング方式によるしきい値結合カオス回路の設計

上ノ原誠二, 厚地泰輔, 松坂建治, 森江隆, 合原一幸

電子情報通信学会 NLP研究会 112 ( 389 ) 105 - 110 2013年01月

Obstacles Extraction from a Video Taken by a Moving Camera 査読有り

S. Qian, J. K. Tan, H. Kim, S. Ishikawa, and T. Morie

1st Int. Conf. on Connected Vehicles & Expo 2012年12月

知的センシグデバイスデバイスのための脳型処理モデルを実現するアナログ・ディジタル融合LSI 招待有り

森江隆, 秦佑輔、西広海、松坂建治

電気学会センサ・マイクロマシン部門大会, 第29回「センサ・マイクロマシンと応用システム」シンポジウム 2012年10月

LSI実装に向けたパルス結合位相振動子系に基づくスパイクベース演算

松坂建治, 田中秀樹, 大久保悟, 東原敬, 森江隆

電子情報通信学会 NC研究会 112 ( 227 ) 127 - 132 2012年10月

トマト果実自動収穫のためのKinectセンサを利用した果実・萼位置検出手法

栗谷康隆, 田中宙夫, 松原弘樹, 清水昌樹, 森江隆

画像の認識・理解シンポジウム IS1-76 2012年08月

A Hierarchical Extension of the HOG Model Implemented in the Convolution-net for Human Detection 査読有り

Y. Arakaki, H. Shouno, K. Takahashi, and T. Morie

IPSJ Transactions on Mathematical Modeling and Its Applications 5 ( 3 ) 54 - 62 2012年08月

Biologically-inspired Learning Device using Three-terminal Ferroelectric Memristor 査読有り

M. Ueda, Y. Kaneko, Y. Nishitani, T. Morie and E. Fujii

70th Device Research Conference 275 - 276 2012年06月

Bicycle Detection Using Pedaling Movement by Spatiotemporal Gabor Filtering 査読有り

K. Takahashi, Y. Kuriya, T. Morie

Int. J. of innovative Computing Information and Control 8 ( 6 ) 2012年06月

Torus-doubling Process via Strange Nonchaotic Attractors 査読有り

T. Mitsui, S. Uenohara, T. Morie, Y. Horio, and K. Aihara

Physics Letters A 376 1907 - 1914 2012年05月

画像処理による道路状態・歩行者の認識,

森江隆, 石川聖二, 松岡　悟

光技術コンタクト ( 社団法人日本オプトメカトロニクス協会 ) 50 ( 6 ) 20 - 24 2012年04月

強誘電体ゲートトランジスタを用いたシナプス素子におけるSTDP機能の実証

.西谷雄, 金子幸広, 上田路人, 森江隆, 藤井映志

電子情報通信学会 NC研究会 111 ( 483 ) 203 - 207 2012年03月

A hierarchical extension of the HOG model implemented in the convolution-net for human detection

Y. Arakaki, H. Shouno, K. Takahashi, and T. Morie

情報処理学会研究報告. MPS, 数理モデル化と問題解決研究報告 2012年02月

A Motion Detection Model Inspired by the Neuronal Propagation in the Hippocampus 査読有り

H. Liang, T. Morie

IEICE Trans. Fundamentals E95-A ( 2 ) 576 - 585 2012年02月

Obstacle detection using a moving camera 査読有り

Qian S., Tan J., Ishikawa S., Morie T.

Proceedings of the 16th International Symposium on Artificial Life and Robotics, AROB 16th'11 767 - 770 2011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）

This paper proposes a method of detecting obstacles from a video taken by a moving camera mounted on a vehicle by background subtraction. The background subtraction is often used to detect moving objects when camera is static. But according to the characteristics of a road, we can also employ Gaussian mixture model to detect all objects (either static or moving objects) on the road in the case of moving camera. Then we use two consecutive image frames, and warps the first image according to the geometrical relationship between these two images. The road area is then extracted by comparing the warped image with the second image. Using this road area, we can delete all things which are not obstacles. In the performed experiments, it is shown that the proposed method is able to detect obstacles such as vehicles and pedestrians on a road. © 2011 ISAROB.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84866717620&origin=inward

Moving objects detection at an intersection by sequential background extraction 査読有り

Sonoda S., Tan J., Kim H., Ishikawa S., Morie T.

International Conference on Control, Automation and Systems 1752 - 1755 2011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）

Recently, there are many traffic accidents in turning right at an intersection. They are mainly caused by a driver's oversight of pedestrians and motorcycles that are occluded by oncoming cars. Therefore a system is necessary to detect moving objects such as oncoming cars and pedestrians at an intersection, and warn a vehicle driver. This paper describes a technique for detecting moving objects in turning right at an intersection when vehicle is stopping. Moving objects are detected by Mixture of Gaussians (MoG). In addition, we distinguish cars from pedestrians using the difference of the area size and the aspect ratio of detected objects. The object which is classified as a pedestrian is tracked using Lucas-Kanade Tracker. If the detected cars and pedestrians overlap or a car completely obscures pedestrians, we perform the estimation of pedestrian's location by using the information on past frames. By doing this, it is possible to detect pedestrians that drivers are actually difficult to see. The performance of the proposed technique was examined employing car videos and satisfactory results were obtained. © 2011 ICROS.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84863041517&origin=inward

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