An Edge Detection Technique for Grayscale Images Based on Fuzzy Logic

Abstract

Edge detection is a preliminary process in many image processing and computer vision applications such as object detection and object extraction. It detects important events in the image where sharp discontinuity in pixels intensity is found. Several edge detection techniques have been proposed including Sobel, Canny, Prewitt, etc. In this paper, an edge detection technique based on fuzzy inference system is proposed. Since fuzzy logic is a powerful tool to manage the uncertainty efficiently, it can be used in edge detection to help in making a decision regarding whether to consider a certain pixel as an edge pixel or not.  A two-phase fuzzy inference system is proposed to detect edges in gray level images. In the first phase the discontinuity in pixels intensity is evaluated according to various directions, while in the second phase the final decision is determined based on the results obtained from the first phase. The proposed algorithm is implemented using MATLAB and the experimental results show improvement when compared with other edge detection techniques.