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Persistence-based Segmentation of Deformable Shapes

Abstract : In this paper, we combine two ideas: persistence-based clustering and the Heat Kernel Signature (HKS) function to obtain a multi-scale isometry invariant mesh segmentation algorithm. The key advantages of this approach is that it is tunable through a few intuitive parameters and is stable under near-isometric deformations. Indeed the method comes with feedback on the stability of the number of segments in the form of a persistence diagram. There are also spatial guarantees on part of the segments. Finally, we present an extension to the method which first detects regions which are inherently unstable and segments them separately. Both approaches are reasonably scalable and come with strong guarantees. We show numerous examples and a comparison with the segmentation benchmark and the curvature function.
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Contributor : Marc Glisse Connect in order to contact the contributor
Submitted on : Thursday, January 10, 2013 - 3:20:53 PM
Last modification on : Friday, February 26, 2021 - 9:30:02 AM


  • HAL Id : hal-00772475, version 1



Frédéric Chazal, Leonidas J. Guibas, Primoz Skraba, Maks Ovsjanikov. Persistence-based Segmentation of Deformable Shapes. CVPR Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, Jun 2010, San Francisco, United States. ⟨hal-00772475⟩



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