Image Edge Detection with Fuzzy Classifier

Our special fuzzy classifier operates on the set of eight features extracted from the 3x3 neighborhood of each pixel. These features are the magnitudes of differences between that pixel and the eight neighboring pixels. They are input into the fuzzy classifier inputs that connect to two fuzzy set membership functions that represent “white background” or “black edge.” The paradigm is simple, computationally efficient, has low sensitivity to noise and is isotropic. Each pixel in the image is mapped to white or black. The fuzzy classifier yields bold black lines on a white background. The benefits of employing a fuzzy classifier for edge detection are its small computation, low sensitivity to noise, isotropy and easy modeling, We discuss these in more detail after introducing the methodology. Methodology. For a 3x3 neighborhood of a center pixel p5, the graylevel difference magnitudes between p5 and its neighbors are designated by X1, X2,...,X8 and calculated by X1=p1-p5 X2=p2-p5 X3=p3-p5 X4=p4-p5