6533b82ffe1ef96bd1295ae7
RESEARCH PRODUCT
Chaotic multiagent system approach for MRF-based image segmentation
Kamal Eddine MelkemiSebti FoufouMohamed Batouchesubject
Segmentation-based object categorizationbusiness.industryComputer scienceMulti-agent systemCrossoverComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONChaoticScale-space segmentationImage segmentationComputingMethodologies_ARTIFICIALINTELLIGENCENonlinear Sciences::Chaotic DynamicsComputer Science::Multiagent SystemsComputerSystemsOrganization_MISCELLANEOUSComputer Science::Computer Vision and Pattern RecognitionIterated conditional modesSegmentationComputer visionArtificial intelligencebusinessdescription
In this paper, we propose a new chaotic approach for image segmentation based on multiagent system (MAS). We consider a set of segmentation agents organized around a coordinator agent. Each segmentation agent performs iterated conditional modes (ICM) starting from its own initial image created using a chaotic mapping. The coordinator agent diversifies the initial images using a crossover and a chaotic mutation operators. The efficiency of our chaotic MAS approach is shown through some experimental results.
year | journal | country | edition | language |
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2005-01-01 | ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005. |