ML Anthology

Renormalization for Unbiased Estimation

Kenichi Kanatani
ICCV 1993 pp. 599-606
doi:10.1109/ICCV.1993.378156 /iccv/1993/kanatani1993iccv-renormalization/

Abstract

In many computer vision problems, it is necessary to robustly estimate parameter values from a large quantity of image data. In such problems, least-squares minimization is computationally the most convenient and practical solution method. The author shows that the least-squares solution is in general statistically biased in the presence of noise. A scheme called renormalization that iteratively removes the statistical bias by automatically adjusting to the image noise is presented. It is applied to the problem of estimating vanishing points and focuses of expansion and conic fitting.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Kanatani. "Renormalization for Unbiased Estimation." IEEE/CVF International Conference on Computer Vision, 1993. doi:10.1109/ICCV.1993.378156

Markdown

[Kanatani. "Renormalization for Unbiased Estimation." IEEE/CVF International Conference on Computer Vision, 1993.](https://mlanthology.org/iccv/1993/kanatani1993iccv-renormalization/) doi:10.1109/ICCV.1993.378156

BibTeX

@inproceedings{kanatani1993iccv-renormalization,
  title     = {{Renormalization for Unbiased Estimation}},
  author    = {Kanatani, Kenichi},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1993},
  pages     = {599-606},
  doi       = {10.1109/ICCV.1993.378156},
  url       = {https://mlanthology.org/iccv/1993/kanatani1993iccv-renormalization/}
}