Implementation of Co-occurrence Matrix and Feature Extraction Methods

The co-occurrence matrix serves as a fundamental technique in image processing and computer vision applications. To implement a co-occurrence matrix, feature extraction becomes essential. Feature extraction represents a critical phase in image processing, involving the derivation of discriminative information from images to facilitate subsequent data processing and analysis.

Various feature extraction methodologies exist for co-occurrence matrices. Our implementation incorporates modular subroutines for extracting individual feature vectors, which can be directly invoked by other programs. These subroutines—designed using optimized algorithms for calculating contrast, correlation, energy, and homogeneity features—are developed through rigorous research and practical validation to ensure computational efficiency and reliability. By leveraging these pre-implemented functions, developers can significantly reduce development time while enhancing extraction accuracy and stability through standardized numerical computation methods.