Multiscale Texture Orientation Analysis using Spectral Total-Variation Decomposition

D. Horesh, G. Gilboa, “Multiscale orientation detection using the total variation transform”, Proc. Scale Space and Variational Methods in Computer Vision (SSVM), 2015.


Multi-level texture separation can considerably improve texture analysis, a significant component in many computer vision tasks.
This paper aims at obtaining precise local texture orientations of images in a multiscale manner, characterizing the main obvious ones as well as the very subtle ones.
We use the total variation spectral framework to decompose the image into its different textural scales. Gabor filter banks are then employed to detect prominent orientations within the multis-cale representation. A necessary condition for perfect texture separation is given, based on the spectral total-variation theory.
We show that using this method we can detect and differentiate a mixture of overlapping textures and obtain with high fidelity a multi-valued orientation representation of the image.