Visual shape recognition neural network using BESOM model

Hiroaki Hasegawa, Masafumi Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
Pages102-105
Number of pages4
EditionPART 3
DOIs
Publication statusPublished - 2010 Nov 8
Event20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece
Duration: 2010 Sep 152010 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6354 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hasegawa, H., & Hagiwara, M. (2010). Visual shape recognition neural network using BESOM model. In Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings (PART 3 ed., pp. 102-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6354 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-15825-4_11