Collection Statistical Modeling for Shapes and Imaging
Organisateur(s)
Date(s)
14/05/2024
Gaussian random fields and anisotropy
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 selfsimilarity and anisotropy by focusing on anisotropic selfsimilar Gaussian fields. 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. Joint work with K. Polisano, L. Condat and V. Perrier
