Texture Representation

Texture representation in computer vision aims to effectively capture and utilize the surface characteristics of objects within images and 3D models for various tasks like image editing, segmentation, and recognition. Current research emphasizes developing robust and efficient methods for encoding texture information, focusing on techniques like transformers, autoencoders, and generative adversarial networks (GANs) to learn disentangled representations of texture and structure. These advancements are improving the accuracy and efficiency of applications ranging from medical image analysis (e.g., skin lesion segmentation) to 3D modeling and remote sensing, where detailed texture information is crucial for accurate interpretation and analysis. The development of more effective texture representations is driving progress in numerous fields by enabling more sophisticated and accurate image and data processing.