Gray Level Co Occurrence Matrix

Gray Level Co-occurrence Matrix (GLCM) analysis is a texture feature extraction technique used primarily in image processing and object recognition to quantify spatial relationships between pixel intensities. Current research focuses on optimizing GLCM feature selection for improved computational efficiency and accuracy in object detection and classification tasks, often employing machine learning algorithms like K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forests (RF), sometimes within ensemble learning frameworks. These advancements are impacting various fields, including medical image analysis (e.g., lung nodule detection, pneumoconiosis staging), industrial applications (e.g., rail defect detection), and autonomous systems (e.g., object detection in navigation). The integration of GLCM with other texture descriptors and deep learning architectures is a growing trend, aiming to enhance the robustness and accuracy of image analysis.