Hessian Eigenfunctions for Triangular Mesh Parameterization

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dc.contributor.authorMejía, Daniel
dc.contributor.authorRuíz, Oscar
dc.contributor.authorCadavid, Carlos A.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.contributor.researchgroupLaboratorio CAD/CAM/CAEspa
dc.date.accessioned2016-11-18T22:34:11Z
dc.date.available2016-11-18T22:34:11Z
dc.date.issued2016
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 meses -- 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%)eng
dc.description.sponsorshipINSTICC; Workflow Management Coalition; SCITEVENTSspa
dc.formatapplication/pdfeng
dc.identifier.citation@conference{grapp16, author={Daniel Mejia and Oscar Ruiz-Salguero and Carlos A. Cadavid}, title={Hessian Eigenfunctions for Triangular Mesh Parameterization}, booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}, year={2016}, pages={75-82}, doi={10.5220/0005668200730080}, isbn={978-989-758-175-5}, }spa
dc.identifier.isbn978-989-758-175-5
dc.identifier.urihttp://hdl.handle.net/10784/9698
dc.language.isoengspa
dc.publisherSCITEPRESS
dc.relation.ispartofProceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol.1, pp.75,82, 2016spa
dc.relation.isversionofhttp://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=9lG0KyeQHsM=&t=1spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.localAcceso abiertospa
dc.subject.keywordGeometry, differentialeng
dc.subject.keywordInterpolation spaceseng
dc.subject.keywordManifolds (Mathematics)eng
dc.subject.keywordParametrizacionesspa
dc.subject.keywordMatriz Hessianaspa
dc.subject.keywordReducción de dimensionalidadspa
dc.subject.lembGEOMETRÍA DIFERENCIALspa
dc.subject.lembESPACIOS DE INTERPOLACIÓNspa
dc.subject.lembVARIEDADES (MATEMÁTICAS)spa
dc.subject.lembFUNCIONES VECTORIALESspa
dc.titleHessian Eigenfunctions for Triangular Mesh Parameterizationeng
dc.typeinfo:eu-repo/semantics/conferencePapereng
dc.typeconferencePapereng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localDocumento de conferenciaspa

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