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Patt. Recog. Lett. 1984, 2, pp 311–317. 25. Pal, S. ; Rosenfeld, A. Image Enhancement and Thresholding by Optimization of Fuzzy Compactness. Pattern Recog. Lett. 1988, 7, pp 77–86. 26. Pal, S. ; Ghosh, A. Index of Area Coverage of Fuzzy Image Subsets and Object Extraction. Pattern Recog. Lett. 1990, 11, pp 831–841. 27. Pal, S. ; Ghosh, A. Fuzzy Geometry in Image Analysis. Fuzzy Sets Syst. 1992, 48(1) pp 23–40. 28. Pal, S. ; Rosenfeld, A. A Fuzzy Medical Axis Transformation Based on Fuzzy Disk. Pattern Recog.

K. A Review on Image Segmentation Techniques. Pattern Recog. 1993, 26, pp 1277–1294. 60. Bezdek, J. ;Pal, S. K. ; Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data; IEEE Press: New York, 1992. 61. Gustafon, D. ; Kessel, W. C. Fuzzy Clustering with a Fuzzy Covariance Matrix; Proc. IEEE CDC; San Diego, CA, 1979, pp 761–766. 62. ; Geva, A. B. Unsupervised Optimal Fuzzy Clustering. IEEE Trans. Patt. Anal. Machine Intell. 1989 11, pp 773– 781. 63. ; Jain, A. K. A Clustering Performance Measure Based on Fuzzy Set Decomposition.

4 shows a result when entropy, compactness, length/height was considered as a feature set for computing distance between two successive frames. Here the abscissa represents the total number of frame distances in the sampled time series, and the ordinate is the compound distance value between two successive images. Each scene consists of six frames. Therefore, a change of scene occurs at every sixth index on the abscissa. The scene separation is denoted with vertical grid lines. The effectiveness of the aforesaid fuzzy geometrical parameter is also demonstrated (89) for recognizing overlapping fingerprints with a multilayer perceptron.

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