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dc.contributor.authorTong, WS
dc.contributor.authorTang, CK
dc.date.accessioned2019-09-25T16:30:24Z
dc.date.available2019-09-25T16:30:24Z
dc.date.created2016-02-03 20:11
dc.date.issued2005
dc.identifieroai:repository.ust.hk:1783.1-29937
dc.identifierIEEE transactions on pattern analysis and machine intelligence, v. 27, (3), 2005, MAR, p. 434-449
dc.identifier0162-8828
dc.identifierhttp://dx.doi.org/10.1109/TPAMI.2005.62
dc.identifierhttp://lbsearch.ust.hk:3210/sfx?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0162-8828&rft.volume=27&rft.issue=3&rft.date=2005&rft.spage=434&rft.epage=449&rft.aulast=Tong&rft.aufirst=WS&rft.atitle=Robust+estimation+of+adaptive+tensors+of+curvature+by+tensor+voting
dc.identifierhttp://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000226300200011
dc.identifierhttp://www.scopus.com/record/display.url?eid=2-s2.0-15044350226&origin=inward
dc.identifier.urihttp://hdl.handle.net/20.500.12424/348225
dc.description.abstractAlthough curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.
dc.languageEnglish
dc.subjectCurvature
dc.subjectCurvature tensor
dc.subjectTensor voting
dc.titleRobust estimation of adaptive tensors of curvature by tensor voting
dc.typeArticle
ge.collectioncodeBH
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:7381375
ge.identifier.permalinkhttps://www.globethics.net/gel/7381375
ge.lastmodificationdate2016-03-22 10:52
ge.submissions0
ge.oai.exportid53
ge.oai.repositoryid2944
ge.oai.setnameHKUST Institutional Repository
ge.oai.setspechkust_ir
ge.oai.streamid1
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttps://dx.doi.org/10.1109/TPAMI.2005.62
ge.linkhttp://lbsearch.ust.hk:3210/sfx?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=0162-8828&rft.volume=27&rft.issue=3&rft.date=2005&rft.spage=434&rft.epage=449&rft.aulast=Tong&rft.aufirst=WS&rft.atitle=Robust+estimation+of+adaptive+tensors+of+curvature+by+tensor+voting
ge.linkhttp://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000226300200011
ge.linkhttp://www.scopus.com/record/display.url?eid=2-s2.0-15044350226&origin=inward


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