3-Dimensional object recognition by evolutional RBF network

Hideki Matsuda, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu

Research output: Contribution to journalConference articlepeer-review


This paper tries to recognize 3-dimensional objects by using an evolutional RBF network. Our proposed RBF network has the structure of preparing four RBFs for each hidden layer unit, selecting based on the Euclid distance between an input image and RBF. This structure can be invariant to 2- dimensional rotation by 90 degree. The other rotational invariance can be achieved by the RBF network. In hidden layer units, the number of RBFs, form, and arrangement are determined using real-coded GA. Computer simulations show object recognition can be done using such a method.

Original languageEnglish
Pages (from-to)556-562
Number of pages7
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2773 PART 1
Publication statusPublished - 2003 Dec 1
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: 2003 Sept 32003 Sept 5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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