Texture Recognition

Texture recognition, the automated identification and classification of surface patterns in images, aims to replicate the human visual system's ability to discern textures. Current research focuses on improving the accuracy and efficiency of texture classification using various approaches, including convolutional neural networks (CNNs), vision transformers (ViTs), and novel feature extraction methods like Gabor filters and gray-level co-occurrence matrices (GLCMs), often combined with ensemble techniques. These advancements are driving progress in diverse fields such as medical image analysis (e.g., disease diagnosis), robotics (e.g., object manipulation and navigation), and image retrieval, where accurate texture understanding is crucial for effective performance.