Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/105781
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Type: | Conference paper |
Title: | Guaranteed outlier removal with mixed integer linear programs |
Author: | Chin, T. Kee, Y. Eriksson, A. Neumann, F. |
Citation: | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol.2016, pp.5858-5866 |
Publisher: | IEEE |
Issue Date: | 2016 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781467388511 |
ISSN: | 1063-6919 |
Conference Name: | 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) (26 Jun 2016 - 1 Jul 2016 : Las Vegas, NV) |
Statement of Responsibility: | Tat-Jun Chin, Yang Heng Kee, Anders Eriksson and Frank Neumann |
Abstract: | The maximum consensus problem is fundamentally important to robust geometric fitting in computer vision. Solving the problem exactly is computationally demanding, and the effort required increases rapidly with the problem size. Although randomized algorithms are much more efficient, the optimality of the solution is not guaranteed. Towards the goal of solving maximum consensus exactly, we present guaranteed outlier removal as a technique to reduce the runtime of exact algorithms. Specifically, before conducting global optimization, we attempt to remove data that are provably true outliers, i.e., those that do not exist in the maximum consensus set. We propose an algorithm based on mixed integer linear programming to perform the removal. The result of our algorithm is a smaller data instance that admits a much faster solution by subsequent exact algorithms, while yielding the same globally optimal result as the original problem. We demonstrate that overall speedups of up to 80% can be achieved on common vision problems1. |
Rights: | © 2016 IEEE |
DOI: | 10.1109/CVPR.2016.631 |
Grant ID: | http://purl.org/au-research/grants/arc/DP160103490 http://purl.org/au-research/grants/arc/DE130101775 http://purl.org/au-research/grants/arc/DP140103400 |
Published version: | http://dx.doi.org/10.1109/cvpr.2016.631 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
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RA_hdl_105781.pdf Restricted Access | Restricted access | 1.14 MB | Adobe PDF | View/Open |
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