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Pose Invariant Shape Prior Segmentation Using Continuous Cuts and Gradient Descent on Lie Groups
Malmö högskola, School of Technology (TS).
Malmö högskola, School of Technology (TS).
Malmö högskola, School of Technology (TS).
2009 (English)In: Scale Space and Variational Methods in Computer Vision;5567, 2009, p. 684-695Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a novel formulation of the Chan-Vese model for pose invariant shape prior segmentation as a continuous cut problem. The model is based on the classic L 2 shape dissimilarity measure and with pose invariance under the full (Lie-) group of similarity transforms in the plane. To overcome the common numerical problems associated with step size control for translation, rotation and scaling in the discretization of the pose model, a new gradient descent procedure for the pose estimation is introduced. This procedure is based on the construction of a Riemannian structure on the group of transformations and a derivation of the corresponding pose energy gradient. Numerically, this amounts to an adaptive step size selection in the discretization of the gradient descent equations. Together with efficient numerics for TV-minimization we get a fast and reliable implementation of the model. Moreover, the theory introduced is generic and reliable enough for application to more general segmentation- and shape-models.

Place, publisher, year, edition, pages
2009. p. 684-695
Series
Lecture Notes in Computer Science, ISSN 0302-9743
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-12607DOI: 10.1007/978-3-642-02256-2_57Local ID: 10055OAI: oai:DiVA.org:mau-12607DiVA, id: diva2:1409654
Conference
Scale Space and Variational Methods in Computer Vision (SSVM), Voss, Norway (2009)
Available from: 2020-02-29 Created: 2020-02-29 Last updated: 2022-06-27Bibliographically approved

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Publisher's full texthttp://www.math.uio.no/conference/ssvm2009/index.html

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Fundana, Ketut

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