Hessian eigenfunctions for triangular mesh parameterization

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dc.contributor.authorMejia, D.
dc.contributor.authorRuiz OE
dc.contributor.authorCadavid, C.
dc.contributor.departmentUniversidad EAFIT. Departamento de Cienciasspa
dc.contributor.researchgroupMatemáticas y Aplicacionesspa
dc.creatorMejia, D.
dc.creatorRuiz OE
dc.creatorCadavid, C.
dc.date.accessioned2021-04-12T13:55:40Z
dc.date.available2021-04-12T13:55:40Z
dc.date.issued2016-02-27
dc.description.abstractHessian Locally Linear Embedding (HLLE) is an algorithm that computes the nullspace of a Hessian functional H for Dimensionality Reduction (DR) of a sampled manifold M. This article presents a variation of classic HLLE for parameterization of 3D triangular meshes. Contrary to classic HLLE which estimates local Hessian nullspaces, the proposed approach follows intuitive ideas from Differential Geometry where the local Hessian is estimated by quadratic interpolation and a partition of unity is used to join all neighborhoods. In addition, local average triangle normals are used to estimate the tangent plane TxM at x ? M instead of PCA, resulting in local parameterizations which reflect better the geometry of the surface and perform better when the mesh presents sharp features. A high frequency dataset (Brain) is used to test our algorithm resulting in a higher rate of success (96.63%) compared to classic HLLE (76.4%). © Copyright 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6160
dc.identifier.isbn9789897581755
dc.identifier.otherSCOPUS;2-s2.0-84968821035
dc.identifier.urihttp://hdl.handle.net/10784/27666
dc.language.isoengeng
dc.publisherSciTePress
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84968821035&partnerID=40&md5=b4de65cbb20488261952db07fe803073
dc.rightsSciTePress
dc.sourceHessian Eigenfunctions For Triangular Mesh Parameterization
dc.subject.keywordAlgorithmseng
dc.subject.keywordComputer visioneng
dc.subject.keywordEigenvalues and eigenfunctionseng
dc.subject.keywordGeometryeng
dc.subject.keywordMesh generationeng
dc.subject.keywordParameterizationeng
dc.subject.keywordStatistical testseng
dc.subject.keywordDifferential geometryeng
dc.subject.keywordDimensionality reductioneng
dc.subject.keywordHessian locally linear embeddingeng
dc.subject.keywordManifold learningeng
dc.subject.keywordMesh parameterizationeng
dc.subject.keywordComputer graphicseng
dc.titleHessian eigenfunctions for triangular mesh parameterizationeng
dc.typeinfo:eu-repo/semantics/conferencePapereng
dc.typeconferencePapereng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
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