A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Modeling the constraints of human hand motion
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Review on Vision-Based Full DOF Hand Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time hand-tracking with a color glove
ACM SIGGRAPH 2009 papers
A multi-view vision-based hand motion capturing system
Pattern Recognition
3D model-based hand tracking using stochastic direct search method
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
3D fingertip and palm tracking in depth image sequences
Proceedings of the 20th ACM international conference on Multimedia
Motion capture of hands in action using discriminative salient points
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization
The Visual Computer: International Journal of Computer Graphics
Socket virtual design based on low cost hand tracking and haptic devices
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
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In this paper we present a model-based framework for hand pose estimation, which relies on the depth and color image sequence input. The proposed framework adopts a divide-and-conquer scheme, and combines fingertip tracking and articulated iterative closest point approach to restore the hand motion. The tracked fingertip positions are used to provide an initial estimation of the hand pose, and articulated ICP are adopted for further refinement. Experiments on both synthetic data and real-world sequences show the hand pose estimation scheme can accurately capture the natural hand motion.