Touch the air: an event-driven framework for interactive environments
Proceedings of the 19th Brazilian symposium on Multimedia and the web
A study on the degrees of freedom in touchless interaction
SIGGRAPH Asia 2013 Technical Briefs
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
| Hi-index | 0.00 |
This paper presents a view-independent hand pose recognition system, which allows the recognition of a limited set of predefined postures from single, low resolution depth images in real time on standard hardware in unconstrained environments. The system consists of three modules: hand segmentation and pose compensation, feature extraction and processing, and hand pose recognition. We use principal component analysis to estimate the hand orientation in space and Flusser moment invariants as image features for visual recognition. The implementation details, classification accuracy and performance measures of the recognition system are reported and discussed. The experimental results show that the system can recognize the pose of two hands at full frame rate with an average total latency lower than 5 ms.