Advanced Algorithm Partitioning of Markov and Color Image Segmentation

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dc.contributor.authorFelix Musau
dc.contributor.authorZou Beiji
dc.contributor.authorMohamed Lamine Toure
dc.date.accessioned2024-08-14T07:25:21Z
dc.date.available2024-08-14T07:25:21Z
dc.date.issued2010
dc.description.abstractThe color vision systems require a first step of classifying pixels in a given image into a discrete set of color classes. In this paper we introduce a new method of algorithm partitioning, and color image segmentation based on similarities or dissimilarities of the pixels. We consider fuzzy segmentation with markov, and normalized cut method. Experiments show these different processes used an effective solution on natural images, and computational efficiency. Finally, the algorithm has proven our process of experiments on gray scale, color, and texture images show promising segmentation results successful
dc.identifier.citationToure, M. L., Beiji, Z., & Musau, F. (2010). Advanced Algorithm Partitioning of Markov and Color Image Segmentation. IEEE Xplore, 719–720.
dc.identifier.urihttps://repository.ru.ac.ke/handle/123456789/54
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofseries3rd International Conference on Computer Science and Information Technology
dc.subjectFuzzylmage
dc.subjectMarkov
dc.subjectNormalizedcut
dc.subjectSegmentation
dc.subjectSimilarities.
dc.titleAdvanced Algorithm Partitioning of Markov and Color Image Segmentation
dc.typeArticle

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