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Authors: Kenichi Kanatani 1 and Prasanna Rangarajan 2

Affiliations: 1 Okayama University, Japan ; 2 Southern Methodist University, United States

Keyword(s): Ellipse fitting, Least squares, Algebraic fit, Error analysis, Bias removal.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: 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 several formats:
Kanatani, K. and Rangarajan, P. (2010). HYPERACCURATE ELLIPSE FITTING WITHOUT ITERATIONS. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP; ISBN 978-989-674-029-0; ISSN 2184-4321, SciTePress, pages 5-12. DOI: 10.5220/0002814500050012

@conference{visapp10,
author={Kenichi Kanatani. and Prasanna Rangarajan.},
title={HYPERACCURATE ELLIPSE FITTING WITHOUT ITERATIONS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP},
year={2010},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002814500050012},
isbn={978-989-674-029-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP
TI - HYPERACCURATE ELLIPSE FITTING WITHOUT ITERATIONS
SN - 978-989-674-029-0
IS - 2184-4321
AU - Kanatani, K.
AU - Rangarajan, P.
PY - 2010
SP - 5
EP - 12
DO - 10.5220/0002814500050012
PB - SciTePress