Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/104485
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dc.contributor.authorLai, T.en
dc.contributor.authorWang, H.en
dc.contributor.authorYan, Y.en
dc.contributor.authorXiao, G.en
dc.contributor.authorSuter, D.en
dc.date.issued2017en
dc.identifier.citationComputer Vision and Image Understanding, 2017; 154:152-165en
dc.identifier.issn1077-3142en
dc.identifier.issn1090-235Xen
dc.identifier.urihttp://hdl.handle.net/2440/104485-
dc.description.abstractAbstract not availableen
dc.description.statementofresponsibilityTaotao Lai, Hanzi Wang, Yan Yan , Guobao Xiao, David Suteren
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2016 Elsevier Inc. All rights reserved.en
dc.subjectEpipolar geometry estimation; multiple structures; guided sampling; joint feature distributionsen
dc.titleEfficient guided hypothesis generation for multi-structure epipolar geometry estimationen
dc.typeJournal articleen
dc.identifier.rmid0030057456en
dc.identifier.doi10.1016/j.cviu.2016.10.003en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP130102524en
dc.identifier.pubid275816-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS14en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]en
Appears in Collections:Computer Science publications

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