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» Modeling and Learning Contact Dynamics in Human Motion
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CVPR
1999
IEEE
14 years 10 months ago
Time-Series Classification Using Mixed-State Dynamic Bayesian Networks
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
BC
1998
94views more  BC 1998»
13 years 7 months ago
Quantization of human motions and learning of accurate movements
This paper presents a mathematical model for the learning of accurate human arm movements. Its main features are that the movement is the superposition of smooth submovements, the ...
Etienne Burdet, Theodore E. Milner
ICPR
2008
IEEE
14 years 9 months ago
Dual generative models for human motion estimation from an uncalibrated monocular camera
We propose a new approach to estimate gait kinematics from image sequences taken by a monocular uncalibrated camera. This approach involves two generative models for gait represen...
Guoliang Fan, Xin Zhang
ECCV
2002
Springer
14 years 9 months ago
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
Hedvig Sidenbladh, Michael J. Black, Leonid Sigal
PAMI
2010
188views more  PAMI 2010»
13 years 6 months ago
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding
—Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, ...
Yan Tong, Jixu Chen, Qiang Ji