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JCST
2010
139views more  JCST 2010»
14 years 8 months ago
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
Dilan Görür, Carl Edward Rasmussen
ISVC
2009
Springer
15 years 4 months ago
Speech-Driven Facial Animation Using a Shared Gaussian Process Latent Variable Model
Abstract. In this work, synthesis of facial animation is done by modelling the mapping between facial motion and speech using the shared Gaussian process latent variable model. Bot...
Salil Deena, Aphrodite Galata
CVPR
2009
IEEE
16 years 4 months ago
Nonrigid Shape Recovery by Gaussian Process Regression
Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangu...
Jianke Zhu, Michael R. Lyu, Steven C. H. Hoi
MCS
2005
Springer
15 years 3 months ago
Mixture of Gaussian Processes for Combining Multiple Modalities
This paper describes a unified approach, based on Gaussian Processes, for achieving sensor fusion under the problematic conditions of missing channels and noisy labels. Under the ...
Ashish Kapoor, Hyungil Ahn, Rosalind W. Picard
PAMI
2006
147views more  PAMI 2006»
14 years 9 months ago
Bayesian Gaussian Process Classification with the EM-EP Algorithm
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
Hyun-Chul Kim, Zoubin Ghahramani