A dynamic neural network model for accelerating preliminary parameterization of 3D triangular mesh surfaces

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dc.contributor.authorYavuz, Erdem
dc.contributor.authorYazıcı, Rıfat
dc.date.accessioned2020-11-21T15:53:38Z
dc.date.available2020-11-21T15:53:38Z
dc.date.issued2019en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThis study proposes an effective and fast preliminary mapping algorithm for 3D triangular mesh surfaces. The proposed method exploits barycentric mapping theory and dynamic neural network for computing parametric coordinates corresponding to vertices of 3D triangular mesh. The dynamic network model iteratively moves internal nodes in 2D parametric space until they convergently reach an equilibrium state. The method effectively computes parametric space coordinates of large meshes (having more than 1.5 K vertices) in less time compared to the traditional method using inverse matrix calculation. The proposed method is tested on many surfaces of varying size, and experimental results prove its efficiency and efficacy. © 2018, The Natural Computing Applications Forum.en_US
dc.identifier.doi10.1007/s00521-017-3332-xen_US
dc.identifier.endpage3701en_US
dc.identifier.issn0941-0643
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85040048340en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3691en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-017-3332-x
dc.identifier.urihttps://hdl.handle.net/11467/3641
dc.identifier.volume31en_US
dc.identifier.wosWOS:000485922300033en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Londonen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDynamic neural networken_US
dc.subjectFlatteningen_US
dc.subjectRecurrent neural networken_US
dc.subjectSurface parameterizationen_US
dc.subjectTriangular meshen_US
dc.titleA dynamic neural network model for accelerating preliminary parameterization of 3D triangular mesh surfacesen_US
dc.typeArticleen_US

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