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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition by Linear Combinations of Models
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Real-time inverse kinematics techniques for anthropomorphic limbs
Graphical Models and Image Processing
Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Global and local deformations of solid primitives
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Specialized mappings and the estimation of human body pose from a single image
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Model-based matching of line drawings by linear combinations of prototypes
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
3D Hand Reconstruction from a Monocular View
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Markerless monocular motion capture using image features and physical constraints
CGI '05 Proceedings of the Computer Graphics International 2005
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
2D and 3d full-body gesture database for analyzing daily human gestures
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
A cognitive architecture for Robotic hand posture learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Depth silhouettes for gesture recognition
Pattern Recognition Letters
Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Bio-inspired Connectionist Architecture for Visual Detection and Refinement of Shapes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
DHM'13 Proceedings of the 4th International conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: healthcare and safety of the environment and transport - Volume Part I
Human limb segmentation in depth maps based on spatio-temporal Graph-cuts optimization
Journal of Ambient Intelligence and Smart Environments
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This paper presents a novel method for reconstructing a 3D human body pose from stereo image sequences based on a top-down learning method. However, it is inefficient to build a statistical model using all training data. Therefore, the training data is hierarchically divided into several clusters to reduce the complexity of the learning problem. In the learning stage, the human body model database is hierarchically constructed by classifying the training data into several sub-clusters with silhouette images. The data of each cluster in the bottom level is represented by a linear combination of examples. In the reconstruction stage, the proposed method hierarchically searches a cluster for the best matching silhouette image using a silhouette history image (SHI). Then, the 3D human body pose is reconstructed from a depth image using a linear combination of examples method. By using depth information to reconstruct 3D human body pose, the similar poses in silhouette images are estimated as different 3D human body poses. The experimental results demonstrate that the proposed method is efficient and effective for reconstructing 3D human body poses.