Hessian eigenfunctions for triangular mesh parameterization
| dc.contributor.author | Mejia, D. | |
| dc.contributor.author | Ruiz OE | |
| dc.contributor.author | Cadavid, C. | |
| dc.contributor.department | Universidad EAFIT. Departamento de Ingeniería Mecánica | spa |
| dc.contributor.researchgroup | Laboratorio CAD/CAM/CAE | spa |
| dc.date.accessioned | 2021-04-16T21:24:57Z | |
| dc.date.available | 2021-04-16T21:24:57Z | |
| dc.date.issued | 2016-02-27 | |
| dc.description.abstract | Hessian 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.identifier | https://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6160 | |
| dc.identifier.isbn | 9789897581755 | |
| dc.identifier.other | SCOPUS;2-s2.0-84968821035 | |
| dc.identifier.uri | http://hdl.handle.net/10784/29503 | |
| dc.language.iso | eng | spa |
| dc.publisher | SciTePress | |
| dc.relation.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84968821035&partnerID=40&md5=b4de65cbb20488261952db07fe803073 | |
| dc.rights | SciTePress | |
| dc.source | Hessian Eigenfunctions For Triangular Mesh Parameterization | |
| dc.subject.keyword | Algorithms | eng |
| dc.subject.keyword | Computer vision | eng |
| dc.subject.keyword | Eigenvalues and eigenfunctions | eng |
| dc.subject.keyword | Geometry | eng |
| dc.subject.keyword | Mesh generation | eng |
| dc.subject.keyword | Parameterization | eng |
| dc.subject.keyword | Statistical tests | eng |
| dc.subject.keyword | Differential geometry | eng |
| dc.subject.keyword | Dimensionality reduction | eng |
| dc.subject.keyword | Hessian locally linear embedding | eng |
| dc.subject.keyword | Manifold learning | eng |
| dc.subject.keyword | Mesh parameterization | eng |
| dc.subject.keyword | Computer graphics | eng |
| dc.title | Hessian eigenfunctions for triangular mesh parameterization | eng |
| dc.type | info:eu-repo/semantics/conferencePaper | eng |
| dc.type | conferencePaper | eng |
| dc.type | info:eu-repo/semantics/publishedVersion | eng |
| dc.type | publishedVersion | eng |
| dc.type.local | Documento de conferencia | spa |