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Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation

Volker Rodehorst, Olaf Hellwich
CVPRW 2006 pp. 103
doi:10.1109/CVPRW.2006.88 /cvprw/2006/rodehorst2006cvprw-genetic/

Abstract

A new robust estimator based on an evolutionary optimization technique is proposed. The general hypothesizeand- verify strategy accelerates the parameter estimation substantially by systematic trial and parallel evaluation without the use of prior information. The method is evaluated by estimation of multi-view relations, i.e. the fundamental matrix. Additionally, some results for the trifocal geometry are presented. However, the general methodology could be used for any problem in which relations can be determined from a minimum number of points.

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Text

Rodehorst and Hellwich. "Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.88

Markdown

[Rodehorst and Hellwich. "Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/rodehorst2006cvprw-genetic/) doi:10.1109/CVPRW.2006.88

BibTeX

@inproceedings{rodehorst2006cvprw-genetic,
  title     = {{Genetic Algorithm SAmple Consensus (GASAC) - A Parallel Strategy for Robust Parameter Estimation}},
  author    = {Rodehorst, Volker and Hellwich, Olaf},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {103},
  doi       = {10.1109/CVPRW.2006.88},
  url       = {https://mlanthology.org/cvprw/2006/rodehorst2006cvprw-genetic/}
}