Target Recognition Using Texture Feature Extraction Based on Gray-Level Co-occurrence Matrix

In this article, we present a texture feature extraction method based on Gray-Level Co-occurrence Matrix (GLCM) for target recognition applications. This approach analyzes spatial relationships between pixels to characterize texture patterns in images, enabling effective object identification. The GLCM method computes statistical texture features by quantifying how frequently pairs of pixels with specific gray-level values occur at defined spatial offsets. Implementation typically involves calculating contrast, correlation, energy, and homogeneity features through matrix operations, which can be coded using image processing libraries like OpenCV or MATLAB's graycomatrix function. This technique finds extensive applications in image processing and computer vision domains, particularly for pattern classification tasks where texture discrimination is crucial. The method's robustness makes it valuable for analyzing complex image data structures, with potential enhancements through multi-scale analysis and machine learning integration. We anticipate this GLCM-based texture extraction methodology will contribute significantly to both research and practical implementations of target recognition systems.