Comparison and Evaluation of Edge Detection using Fuzzy Membership Functions

Digital image processing is widely used by many research oriented fields. Edge detection method is one of the important techniques in image segmentation, which is used to find out exact position of objects in the given image. Edge detection can be achieved by various approaches such as Canny, Prewitt, Sobel, etc. Fuzzy Logic techniques have been used in image understanding applications such as detection of edges, feature extraction, classification, and clustering. Present day‟s membership function plays vital role in all kind of process. In this paper, edge detection can be achieved through fuzzy logic trapezoidal membership function with two different mask options such as 2x2 and 3x3 and the results are analyzed with the help of picture quality measures such as PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error). Proposed method is compared with existing Triangular Membership Function results in the form of 2x2 and 3x3 mask and further results are tabulated based on picture quality measures. KeywordsEdge Detection, Fuzzy Logic, Membership functions, Trapezoidal membership function, Triangular membership function, PSNR, MSE. __________________________________________________*****_________________________________________________