Computing Co-occurrence Matrix and Normalizing Co-occurrence Matrix

In this text, we discuss key concepts in image processing related to texture analysis. These concepts include computing the gray-level co-occurrence matrix (GLCM) which captures spatial relationships between pixel intensities, normalizing the GLCM to account for different image sizes and distributions, and extracting four fundamental texture parameters from the normalized matrix: energy (uniformity measure), entropy (randomness indicator), inertia moment (contrast measurement), and correlation (pixel dependency). From these parameters, we compute an 8-dimensional texture feature vector comprising the mean and standard deviation values of energy, entropy, inertia moment, and correlation. Implementation typically involves using functions like graycomatrix() for GLCM calculation, normalization through probability distribution conversion, and parameter extraction using weighted summations across matrix elements. Understanding these concepts enables better mastery of texture analysis techniques and their practical applications in image processing tasks such as material classification and pattern recognition.