Outlier Removal Using Duality
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Altmetric Citations
Olsson, Carl; Eriksson, Anders; Hartley, Richard
Description
In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method for this task is RANSAC. However, as RANSAC only works on a subset of the images, mismatches in longer point tracks may go undetected. To deal with this problem we would like to have, as a post processing step to RANSAC, a method that works on the entire (or a larger) part of the sequence. In this paper we consider two algorithms for doing this. The...[Show more]
dc.contributor.author | Olsson, Carl | |
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dc.contributor.author | Eriksson, Anders | |
dc.contributor.author | Hartley, Richard | |
dc.coverage.spatial | San Francisco USA | |
dc.date.accessioned | 2015-12-10T23:00:36Z | |
dc.date.created | June 13-18 2010 | |
dc.identifier.isbn | 9781424469857 | |
dc.identifier.uri | http://hdl.handle.net/1885/61415 | |
dc.description.abstract | In this paper we consider the problem of outlier removal for large scale multiview reconstruction problems. An efficient and very popular method for this task is RANSAC. However, as RANSAC only works on a subset of the images, mismatches in longer point tracks may go undetected. To deal with this problem we would like to have, as a post processing step to RANSAC, a method that works on the entire (or a larger) part of the sequence. In this paper we consider two algorithms for doing this. The first one is related to a method by Sim & Hartley where a quasiconvex problem is solved repeatedly and the error residuals with the largest error is removed. Instead of solving a quasiconvex problem in each step we show that it is enough to solve a single LP or SOCP which yields a significant speedup. Using duality we show that the same theoretical result holds for our method. The second algorithm is a faster version of the first, and it is related to the popular method of L1-optimization. While it is faster and works very well in practice, there is no theoretical guarantee of success. We show that these two methods are related through duality, and evaluate the methods on a number of data sets with promising results. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.relation.ispartofseries | Computer Vision and Pattern Recognition Conference (CVPR 2010) | |
dc.source | Proceedings of The 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010) | |
dc.subject | Keywords: Hartley; Multi-view reconstruction; Number of datum; Post processing; Quasiconvex; Theoretical result; Computer vision; Computational methods | |
dc.title | Outlier Removal Using Duality | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2010 | |
local.identifier.absfor | 080104 - Computer Vision | |
local.identifier.ariespublication | u4334215xPUB609 | |
local.type.status | Published Version | |
local.contributor.affiliation | Olsson, Carl, Lund University | |
local.contributor.affiliation | Eriksson, Anders, University of Adelaide | |
local.contributor.affiliation | Hartley, Richard, College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 1450 | |
local.bibliographicCitation.lastpage | 1457 | |
local.identifier.doi | 10.1109/CVPR.2010.5539800 | |
local.identifier.absseo | 970109 - Expanding Knowledge in Engineering | |
dc.date.updated | 2016-02-24T11:02:17Z | |
local.identifier.scopusID | 2-s2.0-77956001554 | |
Collections | ANU Research Publications |
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