Entropy Minimization for Groupwise Planar Shape Co-alignment and its Applications

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We propose an information-theoretic criterion, entropy estimate, for the joint alignment of a group of shape observations drawn from an unknown shape distribution. Employing a nonparametric density estimation technique with implicit shape representation, we minimize the entropy estimate with respect to the pose parameters of similarity transformations based on gradient descent optimization for which we provide implementation details. We demonstrate the capacity of our approach in numerous experiments with an application of building a shape prior to prostate MR image segmentation.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2015-11
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING LETTERS, v.22, no.11, pp.1922 - 1926

ISSN
1070-9908
DOI
10.1109/LSP.2015.2441745
URI
http://hdl.handle.net/10203/200212
Appears in Collection
EE-Journal Papers(저널논문)
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