Ensemble ellipse fitting by spatial median consensus
No Thumbnail Available
Identifiers
Date
2021-08-18
Authors
Thurnhofer-Hemsi, Karl
Lopez-Rubio, Ezequiel
Blazquez-Parra, Elidia Beatriz
Ladron-de-Guevara-Munoz, M. Carmen
de-Cozar-Macias, Oscar David
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier science inc
Abstract
Ellipses are among the most frequently used geometric models in visual pattern recognition and digital image analysis. This work aims to combine the outputs of an ensemble of ellipse fitting methods, so that the deleterious effect of suboptimal fits is alleviated. Therefore, the accuracy of the combined ellipse fit is higher than the accuracy of the individual methods. Three characterizations of the ellipse have been considered by different researchers: algebraic, geometric, and natural. In this paper, the natural characterization has been employed in our method due to its superior performance. Furthermore, five ellipse fitting methods have been chosen to be combined by the proposed consensus method. The experiments include comparisons of our proposal with the original methods and additional ones. Several tests with synthetic and bitmap image datasets demonstrate its great potential with noisy data and the presence of occlusion. The proposed consensus algorithm is the only one that ranks among the first positions for all the tests that were carried out. This demonstrates the suitability of our proposal for practical applications with high occlusion or noise. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Description
MeSH Terms
DeCS Terms
CIE Terms
Keywords
ellipse fitting, conic fitting, ensemble methods, L1-norm, spatial median consensus, Group decision-making, Computer vision, Planar curves, Models, Scale, Approximation, Regression, Surfaces, Fit