Core Functionality of Gabor Filter Implementation

We implemented the core functionality of Gabor filtering using the MATLAB programming language. The Gabor filter effectively extracts texture features from images and plays a crucial role in image processing and computer vision applications. This filter operates on multiple scales and orientations, simulating the human visual system's texture perception mechanism. Through Gabor filtering, we can obtain texture information at different scales and orientations, making it applicable for texture classification, object detection, face recognition, and other computer vision tasks. Our implementation includes: - Creation of Gabor filter banks with customizable parameters (wavelength, orientation, bandwidth) - Convolution operations between input images and Gabor kernels - Magnitude and phase response calculations for feature extraction In our implementation, we carefully considered the selection and optimization of filter parameters to ensure the accuracy and stability of filtering results. The code includes functions for: - Generating Gabor kernels using mathematical formulations of Gaussian-modulated sinusoidal waves - Handling multiple orientations (typically 0°, 45°, 90°, 135°) and scales - Efficient computation using MATLAB's built-in convolution functions The implementation allows researchers and developers to better utilize Gabor filtering capabilities in image processing applications, providing a solid foundation for advanced texture analysis and pattern recognition systems.