Developing user interfaces: ensuring usability through product & process
Developing user interfaces: ensuring usability through product & process
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Capturing Human Hand Motion in Image Sequences
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
An Approach Based on Phonemes to Large Vocabulary Chinese Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Tracking Articulated Hand Motion with Eigen Dynamics Analysis
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bare-hand human-computer interaction
Proceedings of the 2001 workshop on Perceptive user interfaces
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
Articulated Hand Tracking by PCA-ICA Approach
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
Rao-Blackwellised particle filter for tracking with application in visual surveillance
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
A real-time hand tracker using variable-length Markov models of behaviour
Computer Vision and Image Understanding
Tracking articulated objects by learning intrinsic structure of motion
Pattern Recognition Letters
Research on Features Extraction from Frame Image Sequences
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 02
Real-time hand-tracking with a color glove
ACM SIGGRAPH 2009 papers
Riemannian manifold learning for nonlinear dimensionality reduction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Particle Filtering for Large-Dimensional State Spaces With Multimodal Observation Likelihoods
IEEE Transactions on Signal Processing - Part I
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Taken it into consideration that human has a great deal of experiences and knowledge of hand postures, if these operating skills of postures are applied to HCI, the simple and convenient human-computer interface can be expected. In fact, tracking, recognition and interaction based on 3D freehand are a part of the cores in our virtual assembly system, but it is a challenging task to track 3D freehand in real-time because of high dimensionality of 3D full hand model. A novel framework for 3D freehand tracking is put forward in this paper. Firstly, we model and investigate this problem under our virtual assembly system (VAS), so as to decrease the arbitrariness and complexity of this issue. Secondly, we put emphasis on building cognitive and behavioral model (CBM) for users in VAS. Thirdly, we research on the way to track 3D freehand based on CBM. The main contributions of this paper are that we propose a new CBM, TPTM model, provide a way to connect users and computer for effective interaction, and present a real-time freehand tracking algorithm. Based on TPTM model, the prediction, the number of particles, the way and scope of sampling, are optimized. TPTM model not only explain behavioral characteristics for users but also can effectively guide the design of freehand tracking algorithm. TPTM model also provides a data structure that can facilitate the implementation of the tracking algorithm. Our experimental results show that the proposed approach raises the quality of each sampled particle or avoid sampling "poor" particles which appear with low probability in each frame, and it tracks 3D freehand in real-time with high accuracy. The number of the drawn particles is reduced up to 5 and the tracking speed increase up to 81 ms per frame.