Novel Bayesian method for fitting parametric and non-parametric models to noisy data

Michael Werman, Daniel Keren

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)552-558
Number of pages7
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
StatePublished - 1999
Externally publishedYes
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: 23 Jun 199925 Jun 1999

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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