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dc.contributor.authorKamal E. Melkemi-
dc.contributor.authorSebti Foufou-
dc.date.accessioned2014-04-18T11:52:23Z-
dc.date.available2014-04-18T11:52:23Z-
dc.date.issued2014-04-18-
dc.identifier.urihttp://archives.univ-biskra.dz/handle/123456789/2351-
dc.description.abstractThis 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. DOI: 10.2498/cit.1001889 Link http://cit.srce.unizg.hr/index.php/CIT/article/view/1889en_US
dc.language.isoenen_US
dc.subjectimage segmentation, fuzzy logic, Markov, random field, multiagent systems, genetic algorithms, chaotic system.en_US
dc.titleFuzzy Distributed Genetic Approaches for Image Segmentationen_US
dc.typeArticleen_US
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