A generalized fuzzy C-means algorithm with applications to contrast modification and binarization of images


by Richardt, J, Nicklisch-Franken, J and Klette, R
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 Ɵmooth” 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].
Reference:
A generalized fuzzy C-means algorithm with applications to contrast modification and binarization of images (Richardt, J, Nicklisch-Franken, J and Klette, R), In Computers and Artificial Intelligence, volume 15, 1996.
Bibtex Entry:
@article{richardt1996aimages,
author = "Richardt, J and Nicklisch-Franken, J and Klette, R",
journal = "Computers and Artificial Intelligence",
pages = "483--507",
title = "A generalized fuzzy C-means algorithm with applications to contrast modification and binarization of images",
volume = "15",
year = "1996",
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].",
issn = "0232-0274",
issue = "5",
keyword = "Fuzzy algorithms",
keyword = "Image analysis",
language = "eng",
}