Gabor Wavelet for Extracting Image Texture Features

This document presents an implementation of texture feature extraction from images using Gabor wavelets, programmed in MATLAB. Gabor wavelets represent a widely-used image processing technique that effectively analyzes texture information within images. By employing Gabor wavelets, we can capture detailed texture variations and subtle patterns in images, leading to improved understanding of image content. The MATLAB implementation typically involves creating a Gabor filter bank with multiple orientations and scales using functions like gabor or custom implementations with varying wavelengths and theta values. This approach allows flexible parameter adjustment and algorithm optimization through MATLAB's image processing toolbox functions such as imgaborfilt for convolution operations. The extracted features can be organized into feature vectors using statistical measures like mean and standard deviation computed from filter responses. This methodology finds extensive applications in image processing and computer vision domains, particularly in image recognition, image classification, and facial recognition systems. Through the combination of Gabor wavelets and MATLAB programming, we can perform deeper image analysis and processing, resulting in more accurate and detailed characterization of image features.