Gabor Image Texture Feature Extraction

Gabor wavelets bear remarkable similarity to the visual stimulus responses of simple cells in human visual systems. They demonstrate outstanding characteristics in extracting local spatial and frequency domain information of targets. In practical implementation, Gabor filters are typically applied through convolution operations with the input image across multiple orientations and scales, often implemented using functions like cv2.getGaborKernel() in OpenCV or custom MATLAB implementations. Gabor wavelets find applications not only in image processing but also in signal processing and pattern recognition domains. Although they don't form orthogonal bases inherently, they can constitute tight frames under specific parameter configurations, making them widely applicable in signal representation and analysis tasks. The parameter optimization typically involves tuning frequency, orientation, bandwidth, and phase parameters to match specific application requirements. Gabor wavelets exhibit particular sensitivity to image edges, providing excellent directional and scale selection characteristics. This makes them particularly effective in image edge detection and texture analysis tasks, where implementations often involve computing Gabor filter responses across multiple orientations (typically 0°, 45°, 90°, 135°) to capture comprehensive texture information. Code implementations commonly include feature vector construction from filtered responses for subsequent classification or analysis. Furthermore, Gabor wavelets show insensitivity to illumination variations, offering strong adaptability to lighting changes. This characteristic makes them particularly useful in image processing under uneven illumination conditions, as they can maintain consistent feature extraction performance regardless of lighting variations, which is crucial for robust computer vision applications.