Real-Time Fingertip Tracking and Gesture Recognition
IEEE Computer Graphics and Applications
SmartCanvas: a gesture-driven intelligent drawing desk system
Proceedings of the 10th international conference on Intelligent user interfaces
Automatic detection of interaction groups
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
The catchment feature model: a device for multimodal fusion and a bridge between signal and sense
EURASIP Journal on Applied Signal Processing
Turning a page on the digital annotation of physical books
Proceedings of the 2nd international conference on Tangible and embedded interaction
Three dimensional fingertip tracking in stereovision
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
| Hi-index | 0.00 |
Hidden Markov Models have been successfully employed in speech recognition and, more recently, in sign language interpretation. They seem adequate for visual recognition of gestures. In this paper, two problems often eluded are considered. We propose to use the Bayesian information criterion in order to determine the optimal number of models states. We describe the contribution of continuous models in opposite of symbolic ones. Experiments on handwriting gestures show recognition rate between 88% and 100%.