Image Texture Feature Extraction

In image processing, texture feature extraction serves a vital function. By extracting texture characteristics from images, we can obtain valuable information about textural patterns that can be applied to image classification, recognition, and various other applications. This program provides a comprehensive tool for extracting image texture features, enabling better understanding and analysis of textual information in images. The implementation employs multiple algorithms and techniques to compute texture features, including: - Gray-Level Co-occurrence Matrix (GLCM): Calculates statistical measures by analyzing the spatial relationships between pixel intensity values, typically implemented using matrix operations and probability distributions - Local Binary Patterns (LBP): Encodes local texture information by comparing each pixel with its neighbors, often implemented through circular sampling and binary thresholding operations - Gaussian Filtering: Applies smoothing operations to reduce noise while preserving important texture characteristics, using convolution with Gaussian kernels Through these techniques, the program extracts statistical information about various texture features such as contrast, directionality, and homogeneity. These features provide deeper insights into image structure and content, establishing a foundation for subsequent image processing tasks. The implementation typically involves feature vector generation, normalization procedures, and distance metric calculations for effective texture analysis.