Steady state memetic algorithm for partial shape matching

Ender Ozcan, Chilukuri K Mohan

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

4 Scopus citations

Abstract

Shape matching teclmiques are important in machine intelligence, especially in applications such as robotics. Currently, there are three major approaches to shape recognition: statistical, syntactic and neural approaches. This paper presents a fourth approach: evolutionary algorithms. A steady state memetic algorithm is shown to be successful in matclfing shapes even when they are partially obscured, and even in the presence of noise in the input image.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages527-536
Number of pages10
Volume1447
ISBN (Print)3540648917, 9783540648918
StatePublished - 1998
Event7th Annual Conference on Evolutionary Programming, EP 1998 - San Diego, United States
Duration: Mar 25 1998Mar 27 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1447
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th Annual Conference on Evolutionary Programming, EP 1998
CountryUnited States
CitySan Diego
Period3/25/983/27/98

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Ozcan, E., & Mohan, C. K. (1998). Steady state memetic algorithm for partial shape matching. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1447, pp. 527-536). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1447). Springer Verlag.