Item

Abstract

In this thesis a general approach for detection of symmetric structures in images is presented. Rather than relying on some feature points to extract symmetries, symmetries are described using a probabilistic formulation of image self-similarity. Using a Markov random field we obtain a joint probability distribution describing all assignments of the image to itself. Due to the high dimensionality of this joint distribution, we do not examine this distribution directly, but approximate its marginals in order to gather information about the symmetries with the image. In the case of perfect symmetries this approximation is done using belief propagation. A novel variant of belief propagation is introduced allowing for reliable approximations when dealing with approximate symmetries. We apply our approach to several images ranging from perfect synthetic symmetries to real-world scenarios, demonstrating the capabilities of probabilistic frameworks for symmetry detection.