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

By John G. Webster (Editor)

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The second-order fuzzy correlation is used for assessing such a concept. The local information of the given image is extracted through a modified cooccurrence matrix. The technique proposed here consists of two linguistic variables bright, dark modeled by two fuzzy subsets and a fuzzy region on the gray level histogram. Each of the gray levels of the fuzzy region is assigned to both defined subsets one by one and the second-order fuzzy correlation using modified co-occurrence matrix is calculated.

Man Cyber. 1976, 6, pp 148–151. Stallings, W. Fuzzy Set Theory Versus Bayesian Statistics. IEEE Trans. Syst. Man Cyber. 1977, 7, pp 216–219. Jain, R. Comments on Fuzzy Set Theory Versus Bayesian Statistics. IEEE Trans. Syst. Man Cyber. 1978 8, pp 332– 333. Chatterjee, B. N. Recognition of Handwritten Characters by a Fuzzy Set Theoretic Approach. Proc. Int. Conf. Advances Inform. Sci. Tech. Calcutta, 1982, pp 166–172. ; Peters, L. Fuzzy Feature Description of Handwritten Patterns. Pattern Recog.

The image is thresholded based on a criterion of similarity between gray levels. The second-order fuzzy correlation is used for assessing such a concept. The local information of the given image is extracted through a modified cooccurrence matrix. The technique proposed here consists of two linguistic variables bright, dark modeled by two fuzzy subsets and a fuzzy region on the gray level histogram. Each of the gray levels of the fuzzy region is assigned to both defined subsets one by one and the second-order fuzzy correlation using modified co-occurrence matrix is calculated.

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


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