Description
Textures in images can often be well modeled using self-similar random fields while they may at the same time display anisotropy. The present contribution thus aims at studying jointly self-similarity and anisotropy by focusing on two classic classes of anisotropic self-similar Gaussian fields.\
In a first part, we shall introduce a class of scale-invariant and anisotropic Gaussian fields and show how the anisotropy of the model can be characterized by the sample paths properties of the fields. We derive of these theoretical results a practical procedure to estimate anisotropy.
In the second part of the talk, we study a class of anisotropic and local self similar Gaussian random fields, and relate the orientation of the fields to the anisotropy properties of the texture. Notably, we use this preliminary study to define a new class of Gaussian fields with prescribed orientation. Thereafter, we propose a practical procedure to perform the synthesis of these textures.