|M.Sc Student||Horesh Dikla|
|Subject||Advanced Texture Decomposition in the Spectral TV Domain|
|Department||Department of Electrical Engineering||Supervisor||Professor Guy Gilboa|
|Full Thesis text|
Precise texture separation can considerably improve texture analysis, a significant component in many computer vision tasks. In this paper we introduce a few advanced methods for image decomposition.
First, we aim 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 recently proposed 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 multiscale 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.
Second, we introduce a novel notion of separation surfaces for image decomposition.
A surface is embedded in the spectral total-variation (TV) three dimensional domain and encodes a spatially-varying separation scale. The method allows good separation of textures with gradually varying pattern size, pattern-contrast or illumination. The TV spectral framework is used to decompose the image into a continuum of textural scales. A desired texture, within a scale range, is found by fitting a surface to the local maximal responses in the spectral domain. A band above and below the surface, referred to as the Texture Stratum, defines for each pixel the adaptive scale range of the texture. Based on the decomposition an application is proposed which can attenuate or enhance textures in the image in a very natural and visually convincing manner.