Please use this identifier to cite or link to this item:
https://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. 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 |
DOI: | 10.1109/CVPR.2011.5995608 |
Published version: | http://dx.doi.org/10.1109/cvpr.2011.5995608 |
Appears in Collections: | Aurora harvest Computer Science publications |
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RA_hdl_70691.pdf Restricted Access | Restricted Access | 1.82 MB | Adobe PDF | View/Open |
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