Color Image Edge Detection Based on Fuzzy Rule Based Approach

Abstract

Edge detection method is one of the important techniques in Image Segmentation, which is used to find out the objects in the input image in exact manner. An edge is the boundary between an object and background and it indicates the boundary between overlapping objects. One of the most commonly used operation analysis is edge detection, which is used for enhancing and detecting edges in the image. It removes useless data, noise and frequencies while preserving the important structural properties in an image. Fuzzy Logic techniques have been used in image understanding applications such as detection of edges, feature extraction, classification, and clustering. Fuzzy logic possess the ability to mimic the human mind to employ modes of reasoning that are approximate rather than exact form effectively. Edge detection in gray images has lot of methods to locate edges and it has several set of algorithms to represent it. But the images produce more information in scenes i.e., color images have few numbers of methods to segment it. So, this paper represent color image segmentation methods in the literature and getting to prepare novel segmentation method by extracting the color channels in the RGB image with combined form of masking, filtering and Thresholding methods. This paper discuss about RGB color model and fuzzy membership functions method and particularly explain about the usage of fuzzy membership functions which are used to create different combination of mask with some sort of rules based on RGB channel extraction to scan the separated channel image and include Threshold and filtering concepts for further to produce the output image in well enhanced way.