ML Anthology

Ellipse Fitting with Hyperaccuracy

Ken-ichi Kanatani
ECCV 2006 pp. 484-495
doi:10.1007/11744023_38 /eccv/2006/kanatani2006eccv-ellipse/

Abstract

For fitting an ellipse to a point sequence, ML (maximum likelihood) has been regarded as having the highest accuracy. In this paper, we demonstrate the existence of a “hyperaccurate” method which outperforms ML. This is made possible by error analysis of ML followed by subtraction of high-order bias terms. Since ML nearly achieves the theoretical accuracy bound (the KCR lower bound), the resulting improvement is very small. Nevertheless, our analysis has theoretical significance, illuminating the relationship between ML and the KCR lower bound.

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Cite

Text

Kanatani. "Ellipse Fitting with Hyperaccuracy." European Conference on Computer Vision, 2006. doi:10.1007/11744023_38

Markdown

[Kanatani. "Ellipse Fitting with Hyperaccuracy." European Conference on Computer Vision, 2006.](https://mlanthology.org/eccv/2006/kanatani2006eccv-ellipse/) doi:10.1007/11744023_38

BibTeX

@inproceedings{kanatani2006eccv-ellipse,
  title     = {{Ellipse Fitting with Hyperaccuracy}},
  author    = {Kanatani, Ken-ichi},
  booktitle = {European Conference on Computer Vision},
  year      = {2006},
  pages     = {484-495},
  doi       = {10.1007/11744023_38},
  url       = {https://mlanthology.org/eccv/2006/kanatani2006eccv-ellipse/}
}