A FUZZY DIFFERENCE BASED EDGE DETECTOR

In this paper, a new algorithm for edge detection based on fuzzy concept is suggested. The proposed approach defines dynamic membership functions for different groups of pixels in a 3 by 3 neighborhood of the central pixel. Then, fuzzy distance and $\alpha$-cut theory are applied to detect the edge map by following a simple heuristic thresholding rule to produce a thin edge image. A large number of experiments are employed to confirm the robustness of the proposed algorithm. In the experiments different cases such as normal images, images corrupted by Gaussian noise, and uneven lightening images are involved. The results obtained are compared with some famous algorithms such as Canny and Sobel operators, a competitive fuzzy edge detector, and a statistical based edge detector. The visual and quantitative comparisons show the effectiveness of the proposed algorithm even for those images that were corrupted by strong noise.