6533b7dcfe1ef96bd1271752

RESEARCH PRODUCT

Fuzzy Distributed Genetic Approaches for Image Segmentation

Sebti FoufouKamal Eddine Melkemi

subject

Markov random fieldGeneral Computer ScienceComputer sciencebusiness.industrySegmentation-based object categorizationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationMarkov processImage processingImage segmentationFuzzy logicsymbols.namesakeGenetic algorithmsymbolsSegmentationArtificial intelligencebusiness

description

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.

yearjournalcountryeditionlanguage
2010-01-01Journal of Computing and Information Technology
https://doi.org/10.2498/cit.1001448