Ellipse fitting with hyperaccuracy

Authors:
Kenichi Kanatani
Affiliations:
Department of Computer Science, Okayama University, Okayama, Japan
Venue:
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Year:
2006

Citing 14
Cited 1

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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.