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|Title:||Guaranteed ellipse fitting with a confidence region and an uncertainty measure for centre, axes, and orientation|
van den Hengel, A.
|Citation:||Journal of Mathematical Imaging and Vision, 2015; 52(2):173-199|
|Zygmunt L. Szpak, Wojciech Chojnacki, Anton van den Hengel|
|Abstract:||A simple and fast ellipse estimation method is presented based on optimisation of the Sampson distance serving as a measure of the quality of fit between a candidate ellipse and data points. Generation of ellipses, not just conics, as estimates is ensured through the use of a parametrisation of the set of all ellipses. Optimisation of the Sampson distance is performed with the aid of a custom variant of the Levenberg–Marquardt algorithm. The method is supplemented with a measure of uncertainty of an ellipse fit in two closely related forms. One of these concerns the uncertainty in the algebraic parameters of the fit and the other pertains to the uncertainty in the geometrically meaningful parameters of the fit such as the centre, axes, and major axis orientation. In addition, a means is provided for visualising the uncertainty of an ellipse fit in the form of planar confidence regions. For moderate noise levels, the proposed estimator produces results that are fully comparable in accuracy to those produced by the much slower maximum likelihood estimator. Due to its speed and simplicity, the method may prove useful in numerous industrial applications where a measure of reliability for geometric ellipse parameters is required.|
|Keywords:||Ellipse fitting; maximum likelihood; uncertainty measure; simultaneous confidence region; centre; semi-major and semi-minor axes; orientation|
|Rights:||© Springer Science+Business Media New York 2014|
|Appears in Collections:||Computer Science publications|
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