Consensus Set Maximization with Guaranteed Global Optimality for Robust Geometry Estimation
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Description
Finding the largest consensus set is one of the key ideas used by the original RANSAC for removing outliers in robust-estimation. However, because of its random and non-deterministic nature, RANSAC does not fulfill the goal of consensus set maximization exactly and optimally. Based on global optimization, this paper presents a new algorithm that solves the problem exactly. We reformulate the problem as a mixed integer programming (MIP), and solve it via a tailored branch-and-bound method, where...[Show more]
dc.contributor.author | Li, Hongdong | |
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dc.coverage.spatial | Kyoto Japan | |
dc.date.accessioned | 2015-12-10T22:27:09Z | |
dc.date.created | September 29-October 2 2009 | |
dc.identifier.isbn | 9781424444199 | |
dc.identifier.uri | http://hdl.handle.net/1885/54073 | |
dc.description.abstract | Finding the largest consensus set is one of the key ideas used by the original RANSAC for removing outliers in robust-estimation. However, because of its random and non-deterministic nature, RANSAC does not fulfill the goal of consensus set maximization exactly and optimally. Based on global optimization, this paper presents a new algorithm that solves the problem exactly. We reformulate the problem as a mixed integer programming (MIP), and solve it via a tailored branch-and-bound method, where the bounds are computed from the MIP's convex under-estimators. By exploiting the special structure of linear robust-estimation, the new algorithm is also made efficient from a computational point of view. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.relation.ispartofseries | IEEE International Conference on Computer Vision (ICCV 2009) | |
dc.source | Proceedings of IEEE International Conference on Computer Vision (ICCV 2009) | |
dc.subject | Keywords: Global optimality; Mixed-integer programming; Special structure; Computational efficiency; Computer vision; Estimation; Global optimization; Integer programming; Branch and bound method | |
dc.title | Consensus Set Maximization with Guaranteed Global Optimality for Robust Geometry Estimation | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2009 | |
local.identifier.absfor | 080104 - Computer Vision | |
local.identifier.ariespublication | u4334215xPUB290 | |
local.type.status | Published Version | |
local.contributor.affiliation | Li, Hongdong, College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 1074 | |
local.bibliographicCitation.lastpage | 1080 | |
local.identifier.doi | 10.1109/ICCV.2009.5459398 | |
dc.date.updated | 2016-02-24T10:59:45Z | |
local.identifier.scopusID | 2-s2.0-77953184592 | |
Collections | ANU Research Publications |
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