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DC poleHodnotaJazyk
dc.contributor.authorBindal, Manika
dc.contributor.authorKamat, Venkatesh
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-18T15:38:12Z
dc.date.available2023-10-18T15:38:12Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 323-330.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54440
dc.description.sponsorshipThis work was financially supported by Visvesvaraya PhD Scheme, MeitY, Government of India under Grant MEITY-PHD-1090.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjecttvarová shodacs
dc.subjecttvarová korespondencecs
dc.subjectfunkční mapycs
dc.titleDetail Preserving Non-rigid Shape Correspondencesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedUnderstanding shapes is an organic process for us (humans) as this is fundamental to our interaction with the surrounding world. However, it is daunting for the machines. Any shape analysis task, particularly non-rigid shape correspondence is challenging due to the ever-increasing resolution of datasets available. Shape Correspondence refers to finding a mapping among various shape elements. The functional map framework deals with this problem efficiently by not processing the shapes directly but rather specifying an additional structure on each shape and then performing analysis in the spectral domain of the shapes. To determine the domain, the Laplace-Beltrami operator has been utilized generally due to its capability of capturing the global geometry of the shape. However, it tends to smoothen out high-frequency features of shape, which results in failure to capture fine details and sharp features of shape for the analysis. To capture such high-frequency sharp features of the shape, this work proposes to utilize a Hamiltonian operator with gaussian curvature as an intrinsic potential function to identify the domain. Computationally it is defined at no additional cost, keeps global structural information of the shape intact and preserves sharp details of the shape in order to compute a better point-to-point correspondence map between shapes.en
dc.subject.translatedshape matchingen
dc.subject.translatedshape correspondenceen
dc.subject.translatedfunctional mapsen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.36
dc.type.statusPeer-revieweden
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