ML Anthology

Finding Minimal Parameterizations of Cylindrical Image Manifolds

Michael Dixon, Nathan Jacobs, Robert Pless
CVPRW 2006 pp. 192
doi:10.1109/CVPRW.2006.82 /cvprw/2006/dixon2006cvprw-finding/

Abstract

Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been successful despite the lack of effective tools to parameterize cyclic data sets. This paper offers an initial approach to this problem, and provides for a minimal parameterization of points that are drawn from cylindrical manifolds-data whose (unknown) generative model includes a cyclic and a non-cyclic parameter. Solving for this special case is important for a number of current, practical applications and provides a start toward a general approach to cyclic manifolds. We offer results on synthetic and real data sets and illustrate an application to de-noising cardiac ultrasound images.

CVPRW Semantic Scholar

Cite

Text

Dixon et al. "Finding Minimal Parameterizations of Cylindrical Image Manifolds." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.82

Markdown

[Dixon et al. "Finding Minimal Parameterizations of Cylindrical Image Manifolds." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/dixon2006cvprw-finding/) doi:10.1109/CVPRW.2006.82

BibTeX

@inproceedings{dixon2006cvprw-finding,
  title     = {{Finding Minimal Parameterizations of Cylindrical Image Manifolds}},
  author    = {Dixon, Michael and Jacobs, Nathan and Pless, Robert},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {192},
  doi       = {10.1109/CVPRW.2006.82},
  url       = {https://mlanthology.org/cvprw/2006/dixon2006cvprw-finding/}
}