The Study Of Image Edge Detection Based On Fuzzy Theory

Edge detection is an important problem in image processing and computer vision. In the past few years, a large number of papers on this subject focus on edge detection based on the mathematical models. However, many of the edge detection algorithms based on mathematical models can only detect specific kinds of edges. For example, an optimal step edge detector may be ineffective for a ramp edges. Moreover, the parameters in some of the mathematical models are difficult to determine when little information about the image is known. Edge detection algorithms based on the application of human knowledge (i.e. knowledge-base systems) show their flexibility. Since some human knowledge can be expressed in terms of linguistic rules, the use of fuzzy logic is appropriate. The idea of applying fuzzy logic to experts systems, where If … then rules represent the knowledge of human being, a fuzzy approach to edge detection is a very promising research area. Therefore, a new algorithm of edge detection based on fuzzy theory is proposed. Firstly, the image data are converted into fuzzy space from real space. Each step in our approach is processed in the fuzzy space. Secondly, the fuzzy image data are enhanced for special times, which could reduce the uncertainty in the fuzzy rules. Thirdly, fuzzy bandwidth, fuzzy threshold and other related parameters should be calculated. And then, the fuzzy image data are classified and re-identified using "If … then" fuzzy rules. At last, the fuzzy image data are defuzzified to real space. An edge image comes out. In this process, a window scans the image with a line and row of reiterative pixels, which can make sure the connection of the edge and the detection of the details in the partial image. The proposed approach can strongly restrain the affection of noise, without the need of filtering.Most of the edge detectors are very sensitive to noise. It is easy for human being to identify the noise and useful information, but hard for computers to do that. The proposed approach adopts fuzzy reasoning based on human being's thinking and experiences in order to extract edges without being deceived by the noise which is presented in the data. The process of edge detection is, in fact, a problem of uncertainty. Fuzzy logic has been shown to be very well suitable to address this uncertainty. The performance results of the proposed approach and other traditional ones are compared in images corrupted and uncorrupted by noise respectively, and evaluated using the subjective and objective criteria respectively. The famous fuzzy approach, Pal set, is analyzed and compared with my approach theoretically. Performance results show that in this paper fuzzy sets have been specifically designed to successfully preserve the quality of image details and textures. When the original image is corrupted by less than 80% Gaussian noise or less than 10% Salt & Pepper noise, the proposed approach always performs better than other traditional methods.Finally, a plan of hardware realization for edge detection based on fuzzy theory is presented.