Visual shape recognition neural network using BESOM model

Hiroaki Hasegawa, Masafumi Hagiwara

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ページ102-105
ページ数4
6354 LNCS
エディションPART 3
DOI
出版物ステータスPublished - 2010
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

氏名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 3
6354 LNCS
ISSN(印刷物)03029743
ISSN(電子版)16113349

Other

Other20th International Conference on Artificial Neural Networks, ICANN 2010
Greece
Thessaloniki
期間10/9/1510/9/18

    フィンガープリント

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

これを引用

Hasegawa, H., & Hagiwara, M. (2010). Visual shape recognition neural network using BESOM model. : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 版, 巻 6354 LNCS, pp. 102-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻 6354 LNCS, 番号 PART 3). https://doi.org/10.1007/978-3-642-15825-4_11