Threshold selection based on fuzzy c-partition entropy approach

Thresholding is an important topic for image processing, pattern recognition and computer vision. Selecting thresholds is a critical issue for many applications. The fuzzy set theory has been successfully applied to many areas, such as control, image processing, pattern recognition, computer vision, medicine, social science, etc. It is generally believed that image processing bears some fuzziness in nature. In this paper, we use the concept of fuzzy c-partition and the maximum fuzzy entropy principle to select threshold values for gray-level images. We have conducted experiments on many images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively, and the resulting images can preserve the main features of the components of the original images very well. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.