Complexity reduction of neural network model for local motion detection in motion stereo vision

Hisanao Akima, Susumu Kawakami, Jordi Madrenas, Satoshi Moriya, Masafumi Yano, Koji Nakajima, Masao Sakuraba, Shige Sato

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Spatial perception, in which objects’ motion and positional relationship are recognized, is necessary for applications such as a walking robot and an autonomous car. One of the demanding features of spatial perception in real world applications is robustness. Neural network-based approaches, in which perception results are obtained by voting among a large number of neuronal activities, seem to be promising. We focused on a neural network model for motion stereo vision proposed by Kawakami et al. In this model, local motion in each small region of the visual field, which comprises optical flow, is detected by hierarchical neural network. Implementation of this model into a VLSI is required for real-time operation with low power consumption. In this study, we reduced the computational complexity of this model and showed cell responses of the reduced model by numerical simulation.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
編集者Derong Liu, Shengli Xie, Dongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy
出版社Springer Verlag
ページ830-839
ページ数10
ISBN(印刷版)9783319701356
DOI
出版ステータスPublished - 2017
イベント24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
継続期間: 2017 11 142017 11 18

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10639 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other24th International Conference on Neural Information Processing, ICONIP 2017
国/地域China
CityGuangzhou
Period17/11/1417/11/18

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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