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» Markov Random Field Modeling in Computer Vision
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ICPR
2008
IEEE
14 years 10 months ago
Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process
In this work we present a model that uses a Dirichlet Process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient ...
Haijun Ren, Leon N. Cooper, Liang Wu, Predrag Nesk...
CVPR
2012
IEEE
11 years 11 months ago
Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation
Automated facial expression recognition has received increased attention over the past two decades. Existing works in the field usually do not encode either the temporal evolutio...
Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic
CVPR
2011
IEEE
13 years 4 months ago
Intrinsic Dense 3D Surface Tracking
This paper presents a novel intrinsic 3D surface distance and its use in a complete probabilistic tracking framework for dynamic 3D data. Registering two frames of a deforming 3D ...
Yun Zeng, Chaohui Wang, Yang Wang, David Gu, Dimit...
NIPS
2004
13 years 10 months ago
Conditional Models of Identity Uncertainty with Application to Noun Coreference
Coreference analysis, also known as record linkage or identity uncertainty, is a difficult and important problem in natural language processing, databases, citation matching and m...
Andrew McCallum, Ben Wellner
UAI
2004
13 years 10 months ago
Iterative Conditional Fitting for Gaussian Ancestral Graph Models
Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks to a class of graphs with a global Markov property ...
Mathias Drton, Thomas S. Richardson