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

    Melkemi, K. E. ; Foufou, Sebti. / Fuzzy distributed genetic approaches for image segmentation. In: Journal of Computing and Information Technology. 2010 ; Vol. 18, No. 3. pp. 221-231.
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