MATLAB Implementation of Gabor Filters with Multi-Scale and Multi-Orientation Support

This article presents a MATLAB implementation of Gabor filters. The default configuration utilizes 5 distinct scales and 8 different orientations for filtering operations. These parameters can be adjusted according to specific requirements to achieve optimal filtering results. Gabor filters serve as crucial tools in image processing, particularly useful for edge detection, texture analysis, and feature extraction applications. The implementation typically involves creating complex sinusoidal waves modulated by Gaussian kernels, where the scale parameter controls the spatial extent of the filter while orientation determines the directional sensitivity. Key implementation aspects include: - Generating Gabor kernels using mathematical combinations of Gaussian envelopes and complex sinusoids - Applying filters through convolution operations with input images - Handling frequency domain implementations using FFT for computational efficiency - Configuring wavelength (scale) and orientation parameters through adjustable theta values By employing Gabor filters, researchers can effectively analyze image details and structural patterns, thereby enhancing image understanding and processing capabilities. The MATLAB code structure typically involves parameter initialization, kernel generation, and sequential application of filters across different scales and orientations.