• ANALYSIS OF IMAGE SEGMENTATION THROUGH EDGE DETECTORS

Abstract

Edge detection is a research field within Image processing and Computer vision, in particular within the area of feature extraction. It is extensively used in image segmentation when we want to divide the image into areas corresponding to different objects. Representing an image by its edges has the further advantage that the amount of data is reduced significantly while retaining most of the image information. In this paper edge detection from the application of various 1-directional as well as 8-directional masks or edge detection operators on the images corrupted with different levels of Impulsive noise is presented. Further, 1-Dimensional operators: Kirsch, Prewitt, Sobel and Robinson used for edge detection are applied on RGB (3-planar) images. Subjective and Objective methods are used to evaluate the different edge operators. Results show that 8-directional operators give better performance than 1-directional operators in presence of impulsive noise, which implies as number of orientations increases we get better results and effect of noise decreases.