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Lee, S. U. On the Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy c-Means Technique. Pattern Recog. 1990, 23, pp 935–952. 57. Pal, S. ; Mitra, S. Fuzzy Dynamic Clustering Algorithm. Pattern Recog. Lett. 1990, 11 pp 525–535. 58. ; Bhaswan, K. Adaptive Fuzzy c-shells Clustering, Proc. NAFIPS 91, University of Missouri, Columbia, MO, 1991, pp 195–199. 59. Pal, N. ; Pal, S. K. A Review on Image Segmentation Techniques. Pattern Recog. 1993, 26, pp 1277–1294. 60. Bezdek, J.

Fuzzy Image Processing and Recognition 112. Huang, P. ; Dai, S. K. Image Retrieval by Texture Similarity. Pattern Recog. 2003, 36(3) pp 665–679. 113. -K. Fuzzy Color Histogram and its Use in Color Image Retrieval. IEEE Trans. Image Proc. 2002, 11(8), pp 944–952. 114. ; Gu, J. Similarity-based Online Feature Selection in Content-Based Image Retrieval. IEEE Trans. Image Proc. 2006, 15(3), pp 702–712. 115. ; Kundu, M. ; Chanda, B. Segmentation of Brain MR Images Using Fuzzy Sets and Modified Co-occurrence Matrix; IEEE Proc.

5 1 otherwise (35) As the key of the proposed method is the comparison of fuzzy correlations, we have to normalize those measures, which is done by computing a normalizing factor α according to the following relation: α= CA (xmin : x p ) CB (xq : xmax ) (36) To obtain the segmented version of the gray-level histogram, we add to each of the subsets A and B a gray-level xi picked up from the fuzzy region and form two fuzzy subsets A and B that are associated with the histogram intervals [xmin , xi ] and [xi , xmax ], where x p < xi < xq .

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24.Fuzzy Systems by John G. Webster (Editor)


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