Color-Based Hands Tracking System for Sign Language Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
The Isometric Self-Organizing Map for 3D Hand Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Visual and Kinematic Manifold Models for Tracking
International Journal of Computer Vision
Tracking Hand Rotation and Grasping from an IR Camera Using Cylindrical Manifold Embedding
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Hand shapes vary for different views or hand rotations. In addition, the high degree of freedom of hand configurations makes it difficult to track hand shape variations. This paper presents a new manifold embedding method that models hand shape variations in different hand configurations and in different views due to hand rotation. Instead of traditional silhouette images, the hand shapes are modeled using depth map images, which provides rich shape information invariant to illumination changes. These depth map images vary for different viewing directions, similar to shape silhouettes. Sample data along view circles are collected for all the hand configuration variations. A new manifold embedding method using a 4D torus for modeling low dimensional hand configuration and hand rotation is proposed to model the product of three circular manifolds. After learning nonlinear mapping from the proposed embedding space to depth map images, we can achieve the tracking of arbitrary shape variations with hand rotation using particle filter on the embedding manifold. The experiment results from both synthetic and real data show accurate estimations of hand rotation through the estimation of the view parameters and hand configuration from key hand poses and hand configuration phases.