RT Journal Article
T1 Ensemble ellipse fitting by spatial median consensus
A1 Thurnhofer-Hemsi, Karl
A1 Lopez-Rubio, Ezequiel
A1 Blazquez-Parra, Elidia Beatriz
A1 Ladron-de-Guevara-Munoz, M. Carmen
A1 de-Cozar-Macias, Oscar David
K1 ellipse fitting
K1 conic fitting
K1 ensemble methods
K1 L1-norm
K1 spatial median consensus
K1 Group decision-making
K1 Computer vision
K1 Planar curves
K1 Models
K1 Scale
K1 Approximation
K1 Regression
K1 Surfaces
K1 Fit
AB 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/).
PB Elsevier science inc
SN 0020-0255
YR 2021
FD 2021-08-18
LK https://hdl.handle.net/10668/27869
UL https://hdl.handle.net/10668/27869
LA en
DS RISalud
RD Apr 6, 2025