ML Anthology

Persistence-Based Segmentation of Deformable Shapes

Primoz Skraba, Maks Ovsjanikov, Frédéric Chazal, Leonidas J. Guibas
CVPRW 2010 pp. 45-52
doi:10.1109/CVPRW.2010.5543285 /cvprw/2010/skraba2010cvprw-persistencebased/

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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Cite

Text

Skraba et al. "Persistence-Based Segmentation of Deformable Shapes." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543285

Markdown

[Skraba et al. "Persistence-Based Segmentation of Deformable Shapes." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/skraba2010cvprw-persistencebased/) doi:10.1109/CVPRW.2010.5543285

BibTeX

@inproceedings{skraba2010cvprw-persistencebased,
  title     = {{Persistence-Based Segmentation of Deformable Shapes}},
  author    = {Skraba, Primoz and Ovsjanikov, Maks and Chazal, Frédéric and Guibas, Leonidas J.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2010},
  pages     = {45-52},
  doi       = {10.1109/CVPRW.2010.5543285},
  url       = {https://mlanthology.org/cvprw/2010/skraba2010cvprw-persistencebased/}
}