Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented. The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.

I. Introduction

An edge may be the result of changes in light absorption, color, shade, and texture, and these changes can be used to determine the depth, size, orientation, and surface properties of a digital image [1]. In analyzing the image digitally, edge detection involves filtering irrelevant information to select the edge points. The detection of subtle changes may be mixed up by noise and this depends on the pixel threshold of change that defines an edge. Detection of these continuous edges is very difficult and time consuming especially when an image is corrupted by noise [2].