Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Texture mapping progressive meshes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Least squares conformal maps for automatic texture atlas generation
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Progressive lossless compression of arbitrary simplicial complexes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Restricted delaunay triangulations and normal cycle
Proceedings of the nineteenth annual symposium on Computational geometry
Binarization of Low Quality Text Using a Markov Random Field Model
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Variational shape approximation
ACM SIGGRAPH 2004 Papers
Surface Sculpting with Stochastic Deformable 3D Surfaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Segmentation of 3D Meshes through Spectral Clustering
PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
ACM SIGGRAPH 2005 Papers
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Mesh analysis using geodesic mean-shift
The Visual Computer: International Journal of Computer Graphics
Mesh Segmentation - A Comparative Study
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Feature Classification and Editing
IEEE Transactions on Visualization and Computer Graphics
A roughness measure for 3D mesh visual masking
Proceedings of the 4th symposium on Applied perception in graphics and visualization
Three-Dimensional Surface Mesh Segmentation Using Curvedness-Based Region Growing Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new CAD mesh segmentation method, based on curvature tensor analysis
Computer-Aided Design
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
The mean field theory in EM procedures for Markov random fields
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
A multiscale random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
A local roughness measure for 3D meshes and its application to visual masking
ACM Transactions on Applied Perception (TAP)
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Voronoi-Based extraction of a feature skeleton from noisy triangulated surfaces
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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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).