Abstract
In this paper a method has been developed for automatic edge detection of an digital image. An edge is made up of those pixels at which there is an abrupt change in the intensity. These pixels are known as edge pixels and are connected to give an edge. In this paper we have developed a mamdanis fuzzy inference system in MATLAB 2008 using fuzzy logic tool box. A smallest possible 2X2 window is used as a scanning mask. Mask slides over the whole image pixel by pixel, first horizontally in topmost horizontal line then after reaching at the end of line, it increments to check the next vertical location and it continues till the whole image is scanned. The FIS built has 4 inputs, each input representing a pixel for 2X2 mask, and 1 output that represents pixel under consideration. The rule editor consists of sixteen fuzzy rules. The results thus obtained are compared with Sobel edge operator and Canny edge operator.
Highlights
An image is a single picture which represents something
The proposed system was tested with different images, its performance being compared to that of the Sobel edge detection algorithm and Canny edge detection algorithm in MATLAB environment
The firing order associated with each fuzzy rule were tuned to obtain good results while extracting edges of the image where we used this image as comparative model for the classical Sobel operator, Canny operator and the Fuzzy Inference System (FIS) method
Summary
An image is a single picture which represents something. It may be a picture of a person, of people or animals, or of an outdoor scene, or a microphotograph of an electronic component, or the result of medical imaging. Yasar Becerikli and Tayfun of Kocaeli University, Computer Engineering Department, Izmit, Turkey proposed that an edge detection is one of the most important tasks in image processing as image segmentation, registration and identification are based on edge detection. They proposed that rulebased approach is advantages as it gives permission to adapt some parameters like the edges thickness can be changed by adding new rules or changing output parameters. The work of this paper is concerned with the development of a fuzzy logic rules based algorithm for the detection of edges in an image.
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