imagesegmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of imagesegmentation algorithms, fuzzyc-means clustering ...
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imagesegmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of imagesegmentation algorithms, fuzzyc-means clustering is an effective and concise segmentation algorithm. However, the drawback of FcM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzyc-meanclustering algorithm based on multi-objectiveoptimization. We add a parameter ? to the fuzzy distance measurement formula to improve the multi-objectiveoptimization. The parameter ? can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clusteringcent. Two different experimental results show that the novel fuzzyc-means approach has an efficient performance and computational time while segmenting images by different type of noises.
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