File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.cviu.2010.10.005
- Scopus: eid_2-s2.0-78751649642
- WOS: WOS:000286715900007
- Find via
Supplementary
-
Bookmarks:
- CiteULike: 1
- Citations:
- Appears in Collections:
Article: Fast multiple-view L2 triangulation with occlusion handling
Title | Fast multiple-view L2 triangulation with occlusion handling | ||||||
---|---|---|---|---|---|---|---|
Authors | |||||||
Keywords | Computer vision LMI Multiple-view triangulation Occlusion | ||||||
Issue Date | 2011 | ||||||
Publisher | Academic Press. The Journal's web site is located at http://www.elsevier.com/locate/cviu | ||||||
Citation | Computer Vision And Image Understanding, 2011, v. 115 n. 2, p. 211-223 How to Cite? | ||||||
Abstract | Multiple-view L2 triangulation is a key problem in computer vision. This paper addresses the standard case where all image points are available, and the case where some image points are not available. In the latter case, it is supposed that the unknown image point belongs to a known region such as a line segment or an ellipse, as it happens for instance due to occlusions. For this problem we propose two methods based on linear matrix inequalities (LMIs). The first method, named TFML, exploits the fundamental matrix and is fast (the average computational time with two and three-views is 0.01 and 0.05 s on Matlab) at the expense of possible conservatism, which however it is shown to occur rarely in practice, and which can be immediately detected. The second method, named TPML, exploits the projection matrix, is slower, but allows one to reduce the conservatism by using techniques for optimization over polynomials. Various examples with synthetic and real data illustrate the proposed strategy. © 2010 Elsevier Inc. All rights reserved. | ||||||
Persistent Identifier | http://hdl.handle.net/10722/135116 | ||||||
ISSN | 2023 Impact Factor: 4.3 2023 SCImago Journal Rankings: 1.420 | ||||||
ISI Accession Number ID |
Funding Information: The authors would like to thank the Editors and the Reviewers for their valuable comments. The work presented in this paper was supported in part by HKU Grant 200907176048 and RGC Grants HKU711208E and HKU712808E. | ||||||
References | |||||||
Grants |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chesi, G | en_HK |
dc.contributor.author | Hung, YS | en_HK |
dc.date.accessioned | 2011-07-27T01:28:29Z | - |
dc.date.available | 2011-07-27T01:28:29Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | Computer Vision And Image Understanding, 2011, v. 115 n. 2, p. 211-223 | en_HK |
dc.identifier.issn | 1077-3142 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/135116 | - |
dc.description.abstract | Multiple-view L2 triangulation is a key problem in computer vision. This paper addresses the standard case where all image points are available, and the case where some image points are not available. In the latter case, it is supposed that the unknown image point belongs to a known region such as a line segment or an ellipse, as it happens for instance due to occlusions. For this problem we propose two methods based on linear matrix inequalities (LMIs). The first method, named TFML, exploits the fundamental matrix and is fast (the average computational time with two and three-views is 0.01 and 0.05 s on Matlab) at the expense of possible conservatism, which however it is shown to occur rarely in practice, and which can be immediately detected. The second method, named TPML, exploits the projection matrix, is slower, but allows one to reduce the conservatism by using techniques for optimization over polynomials. Various examples with synthetic and real data illustrate the proposed strategy. © 2010 Elsevier Inc. All rights reserved. | en_HK |
dc.language | eng | en_US |
dc.publisher | Academic Press. The Journal's web site is located at http://www.elsevier.com/locate/cviu | en_HK |
dc.relation.ispartof | Computer Vision and Image Understanding | en_HK |
dc.rights | NOTICE: this is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, 2011, v. 115 n. 2, p. 211-223. DOI: 10.1016/j.cviu.2010.10.005 | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Computer vision | en_HK |
dc.subject | LMI | en_HK |
dc.subject | Multiple-view triangulation | en_HK |
dc.subject | Occlusion | en_HK |
dc.title | Fast multiple-view L2 triangulation with occlusion handling | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Chesi, G:chesi@eee.hku.hk | en_HK |
dc.identifier.email | Hung, YS:yshung@eee.hku.hk | en_HK |
dc.identifier.authority | Chesi, G=rp00100 | en_HK |
dc.identifier.authority | Hung, YS=rp00220 | en_HK |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1016/j.cviu.2010.10.005 | en_HK |
dc.identifier.scopus | eid_2-s2.0-78751649642 | en_HK |
dc.identifier.hkuros | 187529 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-78751649642&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 115 | en_HK |
dc.identifier.issue | 2 | en_HK |
dc.identifier.spage | 211 | en_HK |
dc.identifier.epage | 223 | en_HK |
dc.identifier.isi | WOS:000286715900007 | - |
dc.publisher.place | United States | en_HK |
dc.relation.project | Triangulation of Points in Multiple-View Vision Systems with Uncertain Data | - |
dc.identifier.scopusauthorid | Chesi, G=7006328614 | en_HK |
dc.identifier.scopusauthorid | Hung, YS=8091656200 | en_HK |
dc.identifier.citeulike | 8134095 | - |
dc.identifier.issnl | 1077-3142 | - |