Kenichi Kanatani, Prasanna Rangarajan


This paper presents a new method for fitting an ellipse to a point sequence extracted from images. It is widely known that the best fit is obtained by maximum likelihood. However, it requires iterations, which may not converge in the presence of large noise. Our approach is algebraic distance minimization; no iterations are required. Exploiting the fact that the solution depends on the way the scale is normalized, we analyze the accuracy to high order error terms with the scale normalization weight unspecified and determine it so that the bias is zero up to the second order. We demonstrate by experiments that our method is superior to the Taubin method, also algebraic and known to be highly accurate.


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Paper Citation

in Harvard Style

Kanatani K. and Rangarajan P. (2010). HYPERACCURATE ELLIPSE FITTING WITHOUT ITERATIONS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 5-12. DOI: 10.5220/0002814500050012

in Bibtex Style

author={Kenichi Kanatani and Prasanna Rangarajan},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
SN - 978-989-674-029-0
AU - Kanatani K.
AU - Rangarajan P.
PY - 2010
SP - 5
EP - 12
DO - 10.5220/0002814500050012