MATLAB Implementation of Gabor Filter for Multi-Scale Texture Feature Extraction

In this text, I will introduce the MATLAB implementation of Gabor filters. Gabor filters are powerful tools for extracting image texture features, capable of performing filtering operations at different orientations and scales. This MATLAB code provides a comprehensive implementation that enables users to understand and analyze image texture characteristics effectively. The implementation includes key functions for creating Gabor filter banks, applying multi-scale filtering, and extracting texture descriptors. The code utilizes MATLAB's image processing toolbox to generate Gabor kernels with customizable parameters including wavelength (scale), orientation, aspect ratio, and bandwidth. Through this implementation, users can perform multi-scale Gabor filtering on images and extract texture features across different orientations and scales. The algorithm employs complex Gabor filters to capture both magnitude and phase information, providing robust texture representation. Key implementation aspects include: - Gabor kernel generation using mathematical formulations of Gaussian-modulated sinusoidal waves - Multi-scale processing through wavelength parameter variation - Multi-orientation coverage by rotating filter kernels - Efficient convolution operations using MATLAB's imfilter function - Feature extraction through filter response analysis This implementation offers enhanced capabilities for image analysis and processing, providing more options and possibilities for accurately describing and understanding texture information in images. The code structure allows for easy parameter adjustment and integration with larger image processing pipelines.