Markov Random Fields for Improving 3D Mesh Analysis and Segmentation (English)

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Mesh 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).

  • Title:
    Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
  • Author / Creator:
  • Published in:
  • Publisher:
    The Eurographics Association
  • Place of publication:
    Postfach 8043, 38621 Goslar, Germany
  • Year of publication:
    2008
  • Size:
    8 pages
  • ISBN:
  • ISSN:
  • DOI:
  • Type of media:
    Conference paper
  • Type of material:
    Electronic Resource
  • Language:
    English
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Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
Characterizing Shape Using Conformal Factors
Ben-Chen, Mirela / Gotsman, Craig | 2008
9
3D Object Retrieval using an Efficient and Compact Hybrid Shape Descriptor
Papadakis, Panagiotis / Pratikakis, Ioannis / Theoharis, Theoharis / Passalis, Georgios / Perantonis, Stavros | 2008
17
Isometry-invariant Matching of Point Set Surfaces
Ruggeri, Mauro R. / Saupe, Dietmar | 2008
25
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Lavoué, Guillaume / Wolf, Christian | 2008
33
Part Analogies in Sets of Objects
Shalom, Shy / Shapira, Lior / Shamir, Ariel / Cohen-Or, Daniel | 2008
41
Similarity Score Fusion by Ranking Risk Minimization for 3D Object Retrieval
Akgül, Ceyhun Burak / Sankur, Bülent / Yemez, Yücel / Schmitt, Francis | 2008
49
A Neurofuzzy Approach to Active Learning based Annotation Propagation for 3D Object Databases
Lazaridis, Michalis / Daras, Petros | 2008
57
Face Recognition by SVMs Classification and Manifold Learning of 2D and 3D Radial Geodesic Distances
Berretti, Stefano / Bimbo, Alberto Del / Pala, Pietro / Mata, Francisco Josè Silva | 2008
65
A 3D Face Recognition Algorithm Using Histogram-based Features
Zhou, Xuebing / Seibert, Helmut / Busch, Christoph / Funk, Wolfgang | 2008
73
On-line and Open Platform for 3D Object Retrieval
Bonhomme, Benoit Le / Mustafa, B. / Celakovsky, Sasko / Preda, Marius / Preteux, Francoise / Davcev, D. | 2008
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