A HYBRID EDGE DETECTOR USING FUZZY LOGIC AND MATHEMATICAL MORPHOLOGY

Edge detection concerns the localization of significant variations of the grey level image. Detection of edges in an image is very important step for a complete image understanding system. This paper proposes a new approach to edge detection which adopts fuzzy reasoning to detect edges and mathematical morphology for edge thinning. The results achieved by this algorithm are comparable to the Canny approach. In Canny edge detector we may require many runs using different combinations of the three parameters (two threshold and one sigma values) whereas in the proposed technique only one parameter needs to be set by the user coarsely to get the same results, also the computation load of Canny is higher than the proposed approach.