Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107730
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Type: Conference paper
Title: Efficient globally optimal consensus maximisation with tree search
Author: Chin, T.
Purkait, P.
Eriksson, A.
Suter, D.
Citation: Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015 / vol.07-12-June-2015, pp.2413-2421
Publisher: IEEE
Issue Date: 2015
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781467369640
ISSN: 1063-6919
Conference Name: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (07 Jun 2015 - 12 Jun 2015 : Boston, MA)
Statement of
Responsibility: 
Tat-Jun Chin, Pulak Purkait, Anders Eriksson, and David Suter
Abstract: Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising the criterion is still customarily done by randomised sample-and-test techniques, which do not guarantee optimality of the result. Several globally optimal algorithms exist, but they are too slow to challenge the dominance of randomised methods. We aim to change this state of affairs by proposing a very efficient algorithm for global maximisation of consensus. Under the framework of LP-type methods, we show how consensus maximisation for a wide variety of vision tasks can be posed as a tree search problem. This insight leads to a novel algorithm based on A* search. We propose efficient heuristic and support set updating routines that enable A* search to rapidly find globally optimal results. On common estimation problems, our algorithm is several orders of magnitude faster than previous exact methods. Our work identifies a promising solution for globally optimal consensus maximisation.
Keywords: Estimation, search problems, robustness, three-dimensional displays, linear regression, cameras, computer science
Rights: © 2015 IEEE
RMID: 0030045857
DOI: 10.1109/CVPR.2015.7298855
Grant ID: http://purl.org/au-research/grants/arc/DP130102524
http://purl.org/au-research/grants/arc/DE130101775
Appears in Collections:Computer Science publications

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