ML Anthology

Geometric Analysis of Constrained Curves

Anuj Srivastava, Washington Mio, Xiuwen Liu, Eric Klassen
NeurIPS 2003 pp. 1579-1586
/neurips/2003/srivastava2003neurips-geometric/

Abstract

We present a geometric approach to statistical shape analysis of closed curves in images. The basic idea is to specify a space of closed curves satisfying given constraints, and exploit the differential geometry of this space to solve optimization and inference problems. We demonstrate this approach by: (i) defining and computing statistics of observed shapes, (ii) defining and learning a parametric probability model on shape space, and (iii) designing a binary hypothesis test on this space.

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Cite

Text

Srivastava et al. "Geometric Analysis of Constrained Curves." Neural Information Processing Systems, 2003.

Markdown

[Srivastava et al. "Geometric Analysis of Constrained Curves." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/srivastava2003neurips-geometric/)

BibTeX

@inproceedings{srivastava2003neurips-geometric,
  title     = {{Geometric Analysis of Constrained Curves}},
  author    = {Srivastava, Anuj and Mio, Washington and Liu, Xiuwen and Klassen, Eric},
  booktitle = {Neural Information Processing Systems},
  year      = {2003},
  pages     = {1579-1586},
  url       = {https://mlanthology.org/neurips/2003/srivastava2003neurips-geometric/}
}