ML Anthology

Analysis of the Least Median of Squares Estimator for Computer Vision Applications

Doron Mintz, Peter Meer, Azriel Rosenfeld
CVPR 1992 pp. 621-623
doi:10.1109/CVPR.1992.223126 /cvpr/1992/mintz1992cvpr-analysis/

Abstract

The robust least-median-of-squares (LMedS) estimator, which can recover a model representing only half the data points, was recently introduced in computer vision. Image data, however, is usually also corrupted by a zero-mean random process (noise) accounting for the measurement uncertainties. It is shown that in the presence of significant noise, LMedS loses its high breakdown point property. A different, two-stage approach in which the uncertainty due to noise is reduced before applying the simplest LMedS procedure is proposed. The superior performance of the technique is proved by comparative graphs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

CVPR Semantic Scholar

Cite

Text

Mintz et al. "Analysis of the Least Median of Squares Estimator for Computer Vision Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992. doi:10.1109/CVPR.1992.223126

Markdown

[Mintz et al. "Analysis of the Least Median of Squares Estimator for Computer Vision Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992.](https://mlanthology.org/cvpr/1992/mintz1992cvpr-analysis/) doi:10.1109/CVPR.1992.223126

BibTeX

@inproceedings{mintz1992cvpr-analysis,
  title     = {{Analysis of the Least Median of Squares Estimator for Computer Vision Applications}},
  author    = {Mintz, Doron and Meer, Peter and Rosenfeld, Azriel},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1992},
  pages     = {621-623},
  doi       = {10.1109/CVPR.1992.223126},
  url       = {https://mlanthology.org/cvpr/1992/mintz1992cvpr-analysis/}
}