Elements of information theory
Elements of information theory
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning in graphical models
Introduction to Monte Carlo methods
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A hierarchical community of experts
Learning in graphical models
Digital Pattern Recognition by Moments
Journal of the ACM (JACM)
Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Modeling Visual Patterns by Integrating Descriptive and Generative Methods
International Journal of Computer Vision
Learning Parameterized Models of Image Motion
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Towards 3D hand tracking using a deformable model
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Journal of Artificial Intelligence Research
Live 3D Video in Soccer Stadium
International Journal of Computer Vision
Tracking articulated objects by learning intrinsic structure of motion
Pattern Recognition Letters
Action recognition feedback-based framework for human pose reconstruction from monocular images
Pattern Recognition Letters
Silhouette representation and matching for 3D pose discrimination - A comparative study
Image and Vision Computing
MovieReshape: tracking and reshaping of humans in videos
ACM SIGGRAPH Asia 2010 papers
Monocular human pose tracking using multi frame part dynamics
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
3D human pose recovery from image by efficient visual feature selection
Computer Vision and Image Understanding
Discriminative fusion of shape and appearance features for human pose estimation
Pattern Recognition
Computer Vision and Image Understanding
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We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body pose) random variables, it is difficult to use it directly in pose estimation, since inference is computationally intractable. In contrast, inference with the discriminative model is tractable, but considerably less accurate for the problem of interest. A combined discriminative/generative formulation is derived that leverages the complimentary strengths of both models in a principled framework for articulated pose inference. Two efficient MAP pose estimation algorithms are derived from this formulation; the first is deterministic and the second non-deterministic. Performance of the framework is quantitatively evaluated in estimating articulated pose of both the human hand and human body.