Gabor Features including Mean and Variance

The Gabor feature is a powerful tool in image processing and computer vision, widely used for texture analysis and feature extraction. It employs Gabor filters—wavelet-like filters that simulate human visual cortex responses—to capture localized frequency and orientation information. Key statistical measures such as mean and variance are computed from Gabor filter responses to characterize texture properties effectively. The implementation typically involves convolving input images with a bank of Gabor filters at multiple scales and orientations, followed by extracting statistical moments from the filtered responses. Interface functions for Gabor feature extraction are designed to be concise and user-friendly, often requiring minimal parameters like filter wavelengths, orientations, and kernel sizes. Whether you're a professional image analyst or exploring computer vision technologies, Gabor features provide an essential, precise tool for applications like pattern recognition, biomedical image analysis, and texture classification with straightforward integration into frameworks like OpenCV or MATLAB.