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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/104485
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dc.contributor.authorLai, T.-
dc.contributor.authorWang, H.-
dc.contributor.authorYan, Y.-
dc.contributor.authorXiao, G.-
dc.contributor.authorSuter, D.-
dc.date.issued2017-
dc.identifier.citationComputer Vision and Image Understanding, 2017; 154:152-165-
dc.identifier.issn1077-3142-
dc.identifier.issn1090-235X-
dc.identifier.urihttp://hdl.handle.net/2440/104485-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityTaotao Lai, Hanzi Wang, Yan Yan , Guobao Xiao, David Suter-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2016 Elsevier Inc. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.cviu.2016.10.003-
dc.subjectEpipolar geometry estimation; multiple structures; guided sampling; joint feature distributions-
dc.titleEfficient guided hypothesis generation for multi-structure epipolar geometry estimation-
dc.typeJournal article-
dc.identifier.doi10.1016/j.cviu.2016.10.003-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP130102524-
pubs.publication-statusPublished-
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]-
Appears in Collections:Aurora harvest 3
Computer Science publications

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