Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Guaranteed ellipse fitting with the Sampson distance|
Van Den Hengel, A.
|Citation:||Proceedings of the 12th European Conference on Computer Vision, held in Florence, Italy, 7-13 October, 2012 / A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato and C. Schmid (eds.): pp.87-100|
|Series/Report no.:||Lecture Notes in Computer Science; 7576|
|Conference Name:||European Conference on Computer Vision (12th : 2012 : Florence, Italy)|
|Zygmunt L. Szpak, Wojciech Chojnacki and Anton van den Hengel|
|Abstract:||When faced with an ellipse fitting problem, practitioners frequently resort to algebraic ellipse fitting methods due to their simplicity and efficiency. Currently, practitioners must choose between algebraic methods that guarantee an ellipse fit but exhibit high bias, and geometric methods that are less biased but may no longer guarantee an ellipse solution. We address this limitation by proposing a method that strikes a balance between these two objectives. Specifically, we propose a fast stable algorithm for fitting a guaranteed ellipse to data using the Sampson distance as a data-parameter discrepancy measure. We validate the stability, accuracy, and efficiency of our method on both real and synthetic data. Experimental results show that our algorithm is a fast and accurate approximation of the computationally more expensive orthogonal-distance-based ellipse fitting method. In view of these qualities, our method may be of interest to practitioners who require accurate and guaranteed ellipse estimates.|
|Rights:||© Springer-Verlag Berlin Heidelberg 2012|
|Appears in Collections:||Computer Science publications|
Files in This Item:
|RA_hdl_77053.pdf||Restricted Access||604.55 kB||Adobe PDF||View/Open|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.