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Pattern Recog. 2006, 39(11), pp 2101– 2114. 131. ; Gamba, P. Detection of Urban Structures in SAR Images by Robust Fuzzy Clustering Algorithms: The Ex- 132. 133. 134. 135. 136. 137. 19 ample of Street Tracking. IEEE Trans. Trans. Geosci. Remote Sensing 2001, 39(10), pp 2287–2297. ; Masulli, F. Vector Quantization and Fuzzy Ranks for Image Reconstruction. Image Vision Comput. 2006, press. Pal, S. ; Fuzzy Sets and Decision Making in Color Image Processing. In Artificial Intelligence and Applied CyberneticsGhosal, A.

To represent the uncertainty in physical relations among the primitives, the production rules of a formal grammar are fuzzified to account for the fuzziness in relation among the primitives, thereby increasing the generative power of a grammar. Such a grammar is called fuzzy grammar (100–102). It has been observed (98) that the incorporation of the element of fuzziness in defining “sharp,” “fair,” and “gentle” curves in the grammars enables one to work with a much smaller number of primitives. By introducing fuzziness in the physical relations among the primitives, it was also possible to use the same set of production rules and nonterminals at each stage, which is expected to reduce, to some extent, the time required for parsing in the sense that parsing needs to be done only once at each stage, unlike the case of the non-fuzzy approach, where each string has to be parsed more than once, in general, at each stage.

Murthy, C. A. D Thesis, Indian Statistical Institute, Calcutta, 1988. 6. ; Kirkpatrik, D. ; Seidel, R. On the Shape of a Set of Points in a Plane. IEEE Trans. Inform. Theory, 1983, 29, p 551. 7. Tousant, G. T. Proc. 5th Int. Conf. Patt. Recog. Miami Beach, FL, 1980, pp 1324–1347. 8. Mandal, D. ; Murthy, C. ; Pal, S. K. Determining the Shape of a Pattern Class From Sampled Points in R2 . Int. J. General Syst. 1992, 20, pp 307–339. 9. Mandal, D. ; Murthy, C. ; Pal, S. K. Determining the Shape of a Pattern Class: Extension to RN .

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


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