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キーワード:
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要旨:
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.
