Edge detection using trapezoidal membership function based on fuzzy´s mamdani inference system

Edge detection is the approach used most frequently for segmenting images based on local changes in intensity. It removes useless data, noise and frequencies while preserving the important structural properties in an image. Edge detection can be achieved by various approaches. The past few years have witnessed a rapid growth in the large number and variety of applications of fuzzy logic. Fuzzy Logic techniques have been used in image - understanding applications such as detection of edges, feature extraction, classification, and clustering. Fuzzy logic poses the ability to mimic the human mind to effectively employ modes of reasoning that are approximate rather than exact form. Present day´s membership function plays vital role in all kind of process. The only condition a membership function must really satisfy is that it must vary the ranges between 0 and 1. So, Membership function is applied to detect the edges in the given input image. In this paper, trapezoidal membership function of mamdani type is used to get effective results.