Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116354
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dc.contributor.authorHengel, A.V.D.en
dc.contributor.authorBrooks, M.J.en
dc.contributor.authorChojnacki, W.en
dc.contributor.authorGawley, D.en
dc.date.issued2002en
dc.identifier.citationProceedings of the British Machine Vision Conference 2002, BMVC 2002, 2002 / Rosin, P.L., Marshall, A.D. (ed./s), pp.468-476en
dc.identifier.isbn1901725197en
dc.identifier.isbn9781901725193en
dc.identifier.urihttp://hdl.handle.net/2440/116354-
dc.description.abstractIn recent work the authors proposed a wide-ranging method for estimating parameters that constrain image feature locations and satisfy a constraint not involving image data. The present work illustrates the use of the method with experiments concerning estimation of the fundamental matrix. Results are given for both synthetic and real images. It is demonstrated that the method gives results commensurate with, or superior to, previous approaches, with the advantage of being faster than comparable methods.en
dc.description.statementofresponsibilityAnton van den Hengel, Wojciech Chojnacki, Michael J. Brooks, Darren Gawleyen
dc.language.isoenen
dc.publisherBritish Machine Vision Associationen
dc.rightsCopyright status unknownen
dc.source.urihttp://www.informatik.uni-trier.de/~ley/db/conf/bmvc/bmvc2002.htmlen
dc.titleA new constrained parameter estimator: experiments in fundamental matrix computationen
dc.typeConference paperen
dc.identifier.rmid0030045451en
dc.contributor.conference13th British Machine Vision Conference 2002 (BMVC 2002) (02 Sep 2002 - 05 Sep 2002 : Cardiff, UK)en
dc.identifier.doi10.5244/C.16.45en
dc.identifier.pubid178146-
pubs.library.collectionAustralian Institute for Machine Learning publicationsen
pubs.library.teamDS05en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Australian Institute for Machine Learning publications

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