Two gesture recognition systems for immersive math education of the deaf
Proceedings of the First International Conference on Immersive Telecommunications
View-invariant gesture recognition using 3D optical flow and harmonic motion context
Computer Vision and Image Understanding
A natural interface for sign language mathematics
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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Dynamic Bayesian networks are a powerful representation to describe processes that vary over time inside a stochastic framework. This paper describes an online visual recognition system to recognize a set of five dynamic gestures executed with the user's right hand using dynamic Bayesian networks for recognition. Gestures are oriented to command mobile robots. The system employs a radial scan segmentation algorithm combined with a statistical-based skin detection method to find the candidate face of the user and to track his right-hand. It uses four simple features to describe the user's right-hand movement. Our system is able to recognize these five gestures in real-time with an average recognition rate of 84.01%, better result than using hidden Markov models for recognition.