Markov Random Fields for Improving 3D Mesh Analysis and Segmentation

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dc.contributor.authorLavoué, Guillaumeen_US
dc.contributor.authorWolf, Christianen_US
dc.contributor.editorStavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmannen_US
dc.date.accessioned2013-10-21T18:15:18Z
dc.date.available2013-10-21T18:15:18Z
dc.date.issued2008en_US
dc.description.abstractMesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In this context we present a new method for clustering / labeling a 3D mesh given any field of scalar values associated with its vertices (curvature, density, roughness etc.). Our algorithm is based on Markov Random Fields, graphical probabilistic models. This Bayesian framework allows (1) to integrate both the attributes and the geometry in the clustering, and (2) to obtain an optimal global solution using only local interactions, due to the Markov property of the random field. We have defined new observation and prior models for 3D meshes, adapted from image processing which achieve very good results in terms of spatial coherency of the labeling. All model parameters are estimated, resulting in a fully automatic process (the only required parameter is the number of clusters) which works in reasonable time (several seconds).en_US
dc.description.seriesinformationEurographics 2008 Workshop on 3D Object Retrievalen_US
dc.identifier.isbn978-3-905674-05-7en_US
dc.identifier.issn1997-0463en_US
dc.identifier.urihttps://doi.org/10.2312/3DOR/3DOR08/025-032en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling I.4.6 [Image Processing And Computer Vision]: Segmentation G.3 [Probability And Statistics]: Markov processesen_US
dc.titleMarkov Random Fields for Improving 3D Mesh Analysis and Segmentationen_US
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