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» Latent Gaussian Mixture Regression for Human Pose Estimation
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CVPR
2008
IEEE
14 years 6 months ago
Context and observation driven latent variable model for human pose estimation
Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human...
Abhinav Gupta, Trista Chen, Francine Chen, Don Kim...
CVPR
2008
IEEE
14 years 6 months ago
Regression from patch-kernel
In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of order...
Shuicheng Yan, Xi Zhou, Ming Liu, Mark Hasegawa-Jo...
ICCV
2007
IEEE
14 years 6 months ago
The Joint Manifold Model for Semi-supervised Multi-valued Regression
Many computer vision tasks may be expressed as the problem of learning a mapping between image space and a parameter space. For example, in human body pose estimation, recent rese...
Ramanan Navaratnam, Andrew W. Fitzgibbon, Roberto ...
BMVC
2010
13 years 2 months ago
Local Gaussian Processes for Pose Recognition from Noisy Inputs
Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they ...
Martin Fergie, Aphrodite Galata
3DPH
2009
163views Healthcare» more  3DPH 2009»
13 years 6 months ago
Discriminative Human Full-Body Pose Estimation from Wearable Inertial Sensor Data
Abstract. In this paper, a method is presented that allows reconstructing the full-body pose of a person in real-time, based on the limited input from a few wearable inertial senso...
Loren Arthur Schwarz, Diana Mateus, Nassir Navab