Visual shape recognition neural network using BESOM model

Hiroaki Hasegawa, Masafumi Hagiwara

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we propose a neural network recognizing visual shapes based on the BidirEctional SOM (BESOM) model. The proposed network has 4 features. First, the network is based on the BESOM model, which is a computational model of the cerebral cortex. Second, the Gabor filter, a model of a simple cell in the primary visual area, is used to calculate input features. Third, the network structure mimics the ventral visual pathway of the brain, which is said to recognize visual shapes. Finally, this is the first application of the BESOM model which is large-scale and multi-layer as far as we know. We conducted an experiment to assess the network and confirmed that it can recognize alphabets.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
ページ102-105
ページ数4
PART 3
DOI
出版ステータスPublished - 2010 11 8
イベント20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece
継続期間: 2010 9 152010 9 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)
番号PART 3
6354 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other20th International Conference on Artificial Neural Networks, ICANN 2010
CountryGreece
CityThessaloniki
Period10/9/1510/9/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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