Gabor Filter for Shape Detection and Feature Extraction

Gabor filters are commonly employed for shape detection and feature extraction tasks, particularly in applications like fingerprint image enhancement. This code implements a two-dimensional Gabor filter using MATLAB. The implementation follows this function structure: function [G, gabout] = gaborfilter1(I, Sx, Sy, f, theta) In this function, G represents the filter output matrix while gabout contains the actual filtered image. The filter's behavior can be customized by adjusting the frequency parameter (f) and orientation angle (theta). For instance: f = [0, 2, 4, 8, 16, 32]; theta = [0, pi/3, pi/6, pi/2, 3*pi/4]; For an input image like 'stereo.jpg', this configuration would generate 6x5 = 30 distinct filtered images. Users can select different angle and frequency combinations based on their specific requirements. Typically, the standard deviation parameters Sx and Sy can be set to values like 2 or 4. The implementation was tested using 'cameraman.tif' from MATLAB's image database: I = imread('cameraman.tif'); [G, gabout] = gaborfilter1(I, 2, 4, 16, pi/3); figure, imshow(uint8(gabout)); The algorithm works by creating a complex Gabor kernel that combines Gaussian envelope modulation with complex sinusoidal carrier waves, allowing simultaneous analysis of spatial frequency and orientation information in images.