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dc.contributor.authorHaas, Sylvainen_US
dc.contributor.authorBaskurt, Atillaen_US
dc.contributor.authorDupont, Florenten_US
dc.contributor.authorDenis, Florenceen_US
dc.contributor.editorIoannis Pratikakis and Florent Dupont and Maks Ovsjanikoven_US
dc.date.accessioned2017-04-22T17:17:43Z
dc.date.available2017-04-22T17:17:43Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-030-7
dc.identifier.issn1997-0471
dc.identifier.urihttp://dx.doi.org/10.2312/3dor.20171054
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20171054
dc.description.abstractCompressed Manifold Modes (CMM) were recently introduced as a solution to one of the drawbacks of spectral analysis on triangular meshes. The eigenfunctions of the Laplace-Beltrami operator on a mesh depend on the whole shape which makes them sensitive to local aspects. CMM are solutions of an extended problem that have a compact rather than global support and are thus suitable for a wider range of applications. In order to use CMM in real applications, an extensive test has been performed to better understand the limits of their computation (convergence and speed) according to the compactness parameter, the mesh resolution and the number of requested modes. The contribution of this paper is to propose a robust choice of parameters, the automated computation of an adequate number of modes (or eigenfunctions), stability with mutltiresolution and isometric meshes, and an example application with high potential for shape indexation.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectPicture/Image Generation
dc.subjectLine and curve generation
dc.titleA Framework Based on Compressed Manifold Modes for Robust Local Spectral Analysisen_US
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/3dor.20171054
dc.identifier.pages63-66


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