Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Regression-based Hand Pose Estimation from Multiple Cameras
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time Hand Pose Recognition Using Low-Resolution Depth Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
Real-time hand posture recognition using range data
Image and Vision Computing
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Markerless and efficient 26-DOF hand pose recovery
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Model-Based 3D Hand Pose Estimation from Monocular Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action recognition in cluttered dynamic scenes using Pose-Specific Part Models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Accurate 3D pose estimation from a single depth image
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Efficient regression of general-activity human poses from depth images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
BodyAvatar: creating freeform 3D avatars using first-person body gestures
Proceedings of the 26th annual ACM symposium on User interface software and technology
Hand shape classification using depth data for unconstrained 3D interaction
Journal of Ambient Intelligence and Smart Environments - Ambient and Smart Component Technologies for Human Centric Computing
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Vision based articulated hand pose estimation and hand shape classification are challenging problems. This paper proposes novel algorithms to perform these tasks using depth sensors. In particular, we introduce a novel randomized decision forest (RDF) based hand shape classifier, and use it in a novel multi---layered RDF framework for articulated hand pose estimation. This classifier assigns the input depth pixels to hand shape classes, and directs them to the corresponding hand pose estimators trained specifically for that hand shape. We introduce two novel types of multi---layered RDFs: Global Expert Network (GEN) and Local Expert Network (LEN), which achieve significantly better hand pose estimates than a single---layered skeleton estimator and generalize better to previously unseen hand poses. The novel hand shape classifier is also shown to be accurate and fast. The methods run in real---time on the CPU, and can be ported to the GPU for further increase in speed.