Structure tensor regularization for texture analysis
Adib Akl, Joe Iskandar · 2015
Texture analysis has been a particularly dynamic field with different computer vision and image processing applications. Most of the existing texture analysis techniques yield to significant results in different applications but fail in difficult situations with high sensitivity to noise. Inspired by previous works on texture analysis by structure layer modeling, this paper deals with representing the texture's structure layer using the structure tensor field. Based on texture pattern size approximation, the proposed algorithm investigates the adaptability of the structure tensor to the local geometry of textures by automatically estimating the sub-optimal structure tensor size. An extension of the algorithm targeting non-structured textures is also proposed. Results show that using the proposed tensor size regularization method, relevant local information can be extracted by eliminating the need of repetitive tensor field computation with different tensor size to reach an acceptable performance.