Extracting Image Texture Features in MATLAB

In MATLAB, computer vision algorithms are employed to extract and analyze image texture features, which serve critical roles in image processing and recognition applications. By conducting in-depth analysis of image texture characteristics, one can obtain richer information about image content and structure, enabling more accurate and efficient image processing and recognition tasks. Common approaches include using gray-level co-occurrence matrices (GLCM) through functions like `graycomatrix()` to compute contrast, correlation, energy, and homogeneity features, or employing Gabor filter banks via `gabor()` function for multi-scale texture decomposition. These texture features play a vital role in computer vision applications and are widely implemented in various image processing workflows, including medical image analysis and material classification, where they enhance feature descriptor robustness through statistical pattern recognition methods.