Nearest neighbor search methods for handshape recognition
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Tracking articulated objects by learning intrinsic structure of motion
Pattern Recognition Letters
A variational approach to monocular hand-pose estimation
Computer Vision and Image Understanding
Gradient-based hand tracking using silhouette data
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
A database-based framework for gesture recognition
Personal and Ubiquitous Computing
3D hand tracking for human computer interaction
Image and Vision Computing
Motion capture of hands in action using discriminative salient points
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Hand pose estimation and hand shape classification using multi-layered randomized decision forests
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
No bias left behind: covariate shift adaptation for discriminative 3d pose estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Real-time hand pose estimation using classifiers
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Generalised pose estimation using depth
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Non-parametric hand pose estimation with object context
Image and Vision Computing
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The RVM-based learning method for whole body pose estimation proposed by Agarwal and Triggs is adapted to hand pose recovery. To help overcome the difficulties presented by the greater degree of self-occlusion and the wider range of poses exhibited in hand imagery, the adaptation proposes a method for combining multiple views. Comparisons of performance using single versus multiple views are reported for both synthesized and real imagery, and the effects of the number of image measurements and the number of training samples on performance are explored.