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Article: Estimating the fundamental matrix via constrained least-squares: A convex approach

TitleEstimating the fundamental matrix via constrained least-squares: A convex approach
Authors
KeywordsConvex Optimization
Fundamental Matrix
Linear Matrix Inequality
Stereo Vision
Issue Date2002
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tpami
Citation
Ieee Transactions On Pattern Analysis And Machine Intelligence, 2002, v. 24 n. 3, p. 397-401 How to Cite?
AbstractIn this paper, a new method for the estimation of the fundamental matrix from point correspondences is presented. The minimization of the algebraic error is performed while taking explicitly into account the rank-two constraint on the fundamental matrix. It is shown how this nonconvex optimization problem can be solved avoiding local minima by using recently developed convexification techniques. The obtained estimate of the fundamental matrix turns out to be more accurate than the one provided by the linear criterion, where the rank constraint of the matrix is imposed after its computation by setting the smallest singular value to zero. This suggests that the proposed estimate can be used to initialize nonlinear criteria, such as the distance to epipolar lines and the gradient criterion, in order to obtain a more accurate estimate of the fundamental matrix.
Persistent Identifierhttp://hdl.handle.net/10722/155165
ISSN
2015 Impact Factor: 6.077
2015 SCImago Journal Rankings: 7.653
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChesi, Gen_US
dc.contributor.authorGarulli, Aen_US
dc.contributor.authorVicino, Aen_US
dc.contributor.authorCipolla, Ren_US
dc.date.accessioned2012-08-08T08:32:09Z-
dc.date.available2012-08-08T08:32:09Z-
dc.date.issued2002en_US
dc.identifier.citationIeee Transactions On Pattern Analysis And Machine Intelligence, 2002, v. 24 n. 3, p. 397-401en_US
dc.identifier.issn0162-8828en_US
dc.identifier.urihttp://hdl.handle.net/10722/155165-
dc.description.abstractIn this paper, a new method for the estimation of the fundamental matrix from point correspondences is presented. The minimization of the algebraic error is performed while taking explicitly into account the rank-two constraint on the fundamental matrix. It is shown how this nonconvex optimization problem can be solved avoiding local minima by using recently developed convexification techniques. The obtained estimate of the fundamental matrix turns out to be more accurate than the one provided by the linear criterion, where the rank constraint of the matrix is imposed after its computation by setting the smallest singular value to zero. This suggests that the proposed estimate can be used to initialize nonlinear criteria, such as the distance to epipolar lines and the gradient criterion, in order to obtain a more accurate estimate of the fundamental matrix.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tpamien_US
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.subjectConvex Optimizationen_US
dc.subjectFundamental Matrixen_US
dc.subjectLinear Matrix Inequalityen_US
dc.subjectStereo Visionen_US
dc.titleEstimating the fundamental matrix via constrained least-squares: A convex approachen_US
dc.typeArticleen_US
dc.identifier.emailChesi, G:chesi@eee.hku.hken_US
dc.identifier.authorityChesi, G=rp00100en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/34.990139en_US
dc.identifier.scopuseid_2-s2.0-0036522406en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036522406&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume24en_US
dc.identifier.issue3en_US
dc.identifier.spage397en_US
dc.identifier.epage401en_US
dc.identifier.isiWOS:000174035900008-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChesi, G=7006328614en_US
dc.identifier.scopusauthoridGarulli, A=7003697493en_US
dc.identifier.scopusauthoridVicino, A=7006250298en_US
dc.identifier.scopusauthoridCipolla, R=7006935878en_US

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