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Kerre, E. ; Lemanieu, I. Noise Reduction by Fuzzy Image Filtering. IEEE Trans. Fuzz. Syst. 2003, 11(4), pp 429–436. 46. ; Barner, K. E. Optimization of Partition-based Weighted Sum Filters and Their Application to Image Denoising. IEEE Trans. Image Proc. 2006, 15(7), pp 1900– 1915. 47. -T. Weighted Fuzzy Mean Filters for Image Processing. Fuzzy Sets Syst. 1997, 89(2), pp 157–180. 48. Russo, F. FIRE Operators for Image Processing. Fuzzy Sets Syst. 1999 103(2), pp 265–275. 49. -H. A Soft Double Regularization Approach to Parametric Blind Image Deconvolution.
For further information, readers may consult References 66–69. The article in Reference 66 describes a modified version of the FCM, which incorporates supervised training data. The article of Cannon et al. (67) describes an approach that reduces the computation required for the FCM, by using look up tables, by a factor of six. Another simplified form of FCM in this line is mentioned in Reference 68. The authors in Reference (69) have proposed a new heuristic fuzzy clustering technique and have referred to it as the Fuzzy J-Means (FJM).
Syst. Man Cybern. 1984, 14, pp 524–528. Pal, S. ; Wang, L. Fuzzy Medical Axis Transformation (FMAT): Practical Feasibility. Fuzzy Sets Syst. 1992, 50, pp 15–34. Dyer, C. ; Rosenfeld, A. Thinning Algorithms for Gray-Scale Pictures. IEEE Trans. Patt. Anal. Mach. Intell. 1979, 1, pp 88–89. Pal, S. ; Leigh, A. B. Motion Frame Analysis and Scene Abstraction: Discrimination Ability of Fuzziness Measures. J. Intel. Fuzzy Syst. 1995, 3, pp 247–256. Pal, S. ; Mitra, S. Noisy Fingerprint Classification Using Multi Layered Perceptron with Fuzzy Geometrical and Textual Features.