MATLAB Implementation of Gabor Feature Extraction

This Gabor feature extraction program provides a robust implementation for texture analysis using complex Gabor filters. The program operates by convolving input images with a bank of Gabor filters at multiple orientations and scales, effectively capturing localized frequency information and texture characteristics. Key implementation aspects include: - Generation of Gabor filter kernels with customizable parameters (wavelength, orientation, bandwidth) - Multi-scale and multi-orientation filtering to capture comprehensive texture information - Feature extraction through filter responses including magnitude and phase components - Optional post-processing steps like feature normalization and dimensionality reduction The extracted Gabor features are particularly effective for applications such as image segmentation, object recognition, texture classification, and biometric analysis. The implementation efficiently handles both 2D and 3D texture analysis, making it an essential tool for researchers and practitioners working with texture-based pattern recognition systems. The code structure allows for easy integration with machine learning pipelines and computer vision workflows.