Fuzzy distributed genetic approaches for image segmentation

K. E. Melkemi, Sebti Foufou

Research output: Contribution to journalArticle

Abstract

This paper presents a new image segmentation algorithm (called FDGA-Seg) based on a combination of fuzzy logic, multiagent systems and genetic algorithms. We propose to use a fuzzy representation of the image site labels by introducing some imprecision in the gray tones values. The distributivity of FDGA-Seg comes from the fact that it is designed around a MultiAgent System (MAS) working with two different architectures based on the master-slave and island models. A rich set of experimental segmentation results given by FDGA-Seg is discussed and compared to the ICM results in the last section.

Original languageEnglish (US)
Pages (from-to)221-231
Number of pages11
JournalJournal of Computing and Information Technology
Volume18
Issue number3
DOIs
StatePublished - Jan 1 2010

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Multi agent systems
Image segmentation
Fuzzy logic
Labels
Genetic algorithms

Keywords

  • Chaotic system
  • Fuzzy logic
  • Genetic algorithms
  • Image segmentation
  • Markov random field
  • Multiagent systems

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Fuzzy distributed genetic approaches for image segmentation. / Melkemi, K. E.; Foufou, Sebti.

In: Journal of Computing and Information Technology, Vol. 18, No. 3, 01.01.2010, p. 221-231.

Research output: Contribution to journalArticle

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