ML Anthology

Bicycle Chain Shape Models

Stefan Sommer, Aditya Tatu, Chen Chen, D. R. Jurgensen, Marleen de Bruijne, Marco Loog, Mads Nielsen, François Lauze
CVPRW 2009 pp. 157-163
doi:10.1109/CVPRW.2009.5204053 /cvprw/2009/sommer2009cvprw-bicycle/

Abstract

In this paper we introduce landmark-based pre-shapes which allow mixing of anatomical landmarks and pseudo-landmarks, constraining consecutive pseudo-landmarks to satisfy planar equidistance relations. This defines naturally a structure of Riemannian manifold on these preshapes, with a natural action of the group of planar rotations. Orbits define the shapes. We develop a geodesic generalized procrustes analysis procedure for a sample set on such a preshape spaces and use it to compute principal geodesic analysis. We demonstrate it on an elementary synthetic example as well on a dataset of manually annotated vertebra shapes from x-ray. We re-landmark them consistently and show that PGA captures the variability of the dataset better than its linear counterpart, PCA.

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Cite

Text

Sommer et al. "Bicycle Chain Shape Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204053

Markdown

[Sommer et al. "Bicycle Chain Shape Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/sommer2009cvprw-bicycle/) doi:10.1109/CVPRW.2009.5204053

BibTeX

@inproceedings{sommer2009cvprw-bicycle,
  title     = {{Bicycle Chain Shape Models}},
  author    = {Sommer, Stefan and Tatu, Aditya and Chen, Chen and Jurgensen, D. R. and de Bruijne, Marleen and Loog, Marco and Nielsen, Mads and Lauze, François},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2009},
  pages     = {157-163},
  doi       = {10.1109/CVPRW.2009.5204053},
  url       = {https://mlanthology.org/cvprw/2009/sommer2009cvprw-bicycle/}
}