Fuzzy Set Theory And Its Application In Image Segmentation

Since L.A.Zadeh has put forward fuzzy set theory, fuzzy set theory has become an important intelligent information processing method. Fuzzy clustering algorithms are an important part of fuzzy set theory, and they are one of the fields that fuzzy set theory applied widest, and the achievements are very fruitful. Because of the fuzziness of the image, recently many researchers introduced fuzzy set theory to image processing, especially in image segmentation, image manipulation and edge detection. This paper has studied the application of fuzzy set theory to image segmentation on the basis of the study of fuzzy set theory.The following is what I have done in this paper:1. The paper systematically summed up the fundamental knowledge of fuzzy set theory, and introduced fuzzy clustering algorithms in detail, and analyzed the defects of low convergence speed and sensitivity to the initialization of fuzzy clustering algorithms, and proposed a modified fuzzy clustering algorithm based on GA.2. After having introduced fuzzy clustering segmentation methods, the paper analyzed the reason why fuzzy clustering segmentation methods performed not very well, and introduced the spacial correlation information and formed a two-dimensional histogram to improve the methods.3. Aiming at the problem of a lot of information loss during the period of image segmentation, the paper proposed a fuzzy clustering segmentation method based on D-S evidence theory, and adopted D-S evidence theory to integrate the pixels' gray information with the spacial correlation information, and gained a satisfactory result.