ML Anthology

A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data

Michael Werman, Daniel Keren
CVPR 1999 pp. 2552-2558
doi:10.1109/CVPR.1999.784964 /cvpr/1999/werman1999cvpr-novel/

Abstract

We offer a simple paradigm for fitting models, parametric and non-parametric, to noisy data, which resolves some of the problems associated with classic MSE algorithms. This is done by considering each point on the model as a possible source for each data point. The paradigm also allows to solve problems which are not defined in the classical MSE approach, such as fitting a segment (as opposed to a line). It is shown to be non-biased, and to achieve excellent results for general curves, even in the presence of strong discontinuities. Results are shown for a number of fitting problems, including lines, circles, segments, and general curves, contaminated by Gaussian and uniform noise.

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Text

Werman and Keren. "A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784964

Markdown

[Werman and Keren. "A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/werman1999cvpr-novel/) doi:10.1109/CVPR.1999.784964

BibTeX

@inproceedings{werman1999cvpr-novel,
  title     = {{A Novel Bayesian Method for Fitting Parametric and Non-Parametric Models to Noisy Data}},
  author    = {Werman, Michael and Keren, Daniel},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {2552-2558},
  doi       = {10.1109/CVPR.1999.784964},
  url       = {https://mlanthology.org/cvpr/1999/werman1999cvpr-novel/}
}