Edge Detection using Fuzzy Logic in Matlab

This paper proposes the implementation of a very simple but efficient fuzzy logic based algorithm to detect the edges of an image without determining the threshold value. The proposed approach begins by scanning the images using floating 3x3 pixel window. Fuzzy inference system designed has 8 inputs, whic h corresponds to 8 pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is "black", "white" or "edge" pixel. Rule base comprises of sixteen rules, w hich classify the target pixel. The proposed method results for different captured images are compared to those obtained with the linear Sobel operator. Images have always been very important in human life. Soft Co mputing is an emerging field that consists of major seminal theories which include fu zzy logic, genetic algorith ms, evolutionary computation, and neural networks In the last few years there is an increasing interest on using soft computing (SC) techniques to solve image processing real-world problems covering a wide range of domains. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Edge detection i is usually done with a first and/or second derivative measurement following by a comparison with threshold which marks the pixel as either belonging to an edge or not. The result is a binary image wh ich contains only the detected edge pixels. Usage of specific linear time-invariant (LTI) filter is the most common procedure applied to the edge detection problem, and the one which results in the least computational effort. In the case of first-order filters, an edge is interpreted as an abrupt variation in gray level between two neighbor pixels. A very important role is played in image analysis by what are termed feature points, pixels that are identified as having a special property. Feature points include edge pixels as determined by the well-known classic edge detectors of PreWitt, Sobel, Marr, and Canny Recent research has concerned using neural Fuzzy Feature to develop edge detectors, after training on a relatively s mall set of proto-type edges, in sample images classifiable by Classic edge detectors. This work was pioneered by Bezdek et. al, (9) who trained a neural net to give the same fu zzy output as a normalized Sobel Operator. In the system described in (7, 8), all inputs to the fuzzy inference systems (FIS) system are obtained by applying to the original image a high-pass filter, a first- order edge detector filter (Sobel operator) and a low-pass (mean) filter. The whole structure is then tuned to function as a contrast enhancing filter and, in another problem, to segment images in a specified number of input classes. The adopted fuzzy ru les and the fuzzy membership functions are specified according to the kind of filtering to be executed. The work o f this paper is concerned with the development of a Fu zzy logic rules based algorithm for the detection of image edges. By scanning the images using floating 3x3 pixel window mask .Fu zzy In ference based system in MATLAB Environment has been developed, wh ich is capable of detecting edges of an image. The rule -base of 28 rules has been designed to mark the pixel under consideration as Black, White or Edge. The result has been compared with the standard algorithms