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» A probabilistic framework for semi-supervised clustering
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3DOR
2008
13 years 10 months ago
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In t...
Guillaume Lavoué, Christian Wolf
TMI
2010
182views more  TMI 2010»
13 years 6 months ago
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data
Abstract—We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated ob...
Seyoung Kim, Padhraic Smyth, Hal S. Stern
CVPR
2005
IEEE
14 years 10 months ago
MosaicShape: Stochastic Region Grouping with Shape Prior
A method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multiscale curvature...
Jingbin Wang, Erdan Gu, Margrit Betke
IVC
2006
154views more  IVC 2006»
13 years 7 months ago
Manifold based analysis of facial expression
We propose a novel approach for modeling, tracking and recognizing facial expressions. Our method works on a low dimensional expression manifold, which is obtained by Isomap embed...
Ya Chang, Changbo Hu, Rogerio Feris, Matthew Turk
DAGM
1998
Springer
14 years 2 days ago
Discrete Mixture Models for Unsupervised Image Segmentation
This paper introduces a novel statistical mixture model for probabilistic clustering of histogram data and, more generally, for the analysis of discrete co occurrence data. Adoptin...
Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann