A Generalized Fuzzy C-means Algorithm with Applications to Contrast Modification and Binarization of Images

Abstract

The fuzzy c-means algorithm (FCM) can be applied to several problems in image analysis, ranging from image segmentation [15, 16] to the detection of pictorial patterns [2,3,4,9]. In this paper it is shown that the problems of image binarization and of segmentation of gray  value histograms are closely related to the basic concepts of the FCM. The binarization can be performed by means of "smooth" contrast modifications at several degrees of sharpness. This is due to the fuzzy thresholding technique supplied by the FCM approach. This paper connects fuzzy thresholding with the known sigmoid functions of neural nets, which serve for the same purpose of fuzzy thresholding.  From there a connection arises between the FCM approach and the basic formulas of simulated annealing [12].