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» Global Connectivity Potentials for Random Field Models
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ICCV
2007
IEEE
15 years 3 months ago
Real-Time Automatic Kinematic Model Building for Optical Motion Capture Using a Markov Random Field
Abstract. We present a completely autonomous algorithm for the real-time creation of a moving subject’s kinematic model from optical motion capture data and with no a priori info...
Stjepan Rajko, Gang Qian
74
Voted
ICML
2004
IEEE
15 years 10 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
NHM
2010
71views more  NHM 2010»
14 years 4 months ago
Non-existence of positive stationary solutions for a class of semi-linear PDEs with random coefficients
We consider a so-called random obstacle model for the motion of a hypersurface through a field of random obstacles, driven by a constant driving field. The resulting semilinear par...
Jérôme Coville, Nicolas Dirr, Stephan...
101
Voted
CVPR
2008
IEEE
15 years 11 months ago
Combining appearance models and Markov Random Fields for category level object segmentation
Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider obje...
Diane Larlus, Frédéric Jurie
VLSM
2005
Springer
15 years 2 months ago
Entropy Controlled Gauss-Markov Random Measure Field Models for Early Vision
We present a computationally efficient segmentationrestoration method, based on a probabilistic formulation, for the joint estimation of the label map (segmentation) and the para...
Mariano Rivera, Omar Ocegueda, José L. Marr...