Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/70691
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Type: Conference paper
Title: A global optimization approach to robust multi-model fitting
Author: Yu, J.
Chin, T.J.
Suter, D.
Citation: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11): pp.2041-2048
Publisher: IEEE
Publisher Place: 345 E 47TH ST, NEW YORK, NY 10017 USA
Issue Date: 2011
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781457703935
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)
Statement of
Responsibility: 
Jin Yu, Tat-Jun Chin and David Suter
Abstract: We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of geometric structures in vision data. Our objective function enforces both the fidelity of a model to the data and the similarity between its associated inliers. Departing from most previous optimization-based approaches, the outcome of our method is a ranking of a given set of putative models, instead of a pre-specified number of “good” candidates (or an attempt to decide the right number of models). This is particularly useful when the number of structures in the data is a priori unascertainable due to unknown intent and purposes. Another key advantage of our approach is that it operates in a unified optimization framework, and the standard QP form of our problem formulation permits globally convergent optimization techniques. We tested our method on several geometric multi-model fitting problems on both synthetic and real data. Experiments show that our method consistently achieves state-of-the-art results.
Rights: Copyright status unknown
RMID: 0020114981
DOI: 10.1109/CVPR.2011.5995608
Appears in Collections:Computer Science publications

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