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Bayesian fusion of hidden Markov models for understanding bimanual movements
Shamaie, Atid; Sutherland, Alistair
Understanding hand and body gestures is a part of a wide spectrum of current research in computer vision and human-computer interaction. A part of this can be the recognition of movements in which the two hands move simultaneously to do something or imply a meaning. We present a Bayesian network for fusing hidden Markov models in order to recognise a bimanual movement. A bimanual movement is tracked and segmented by a tracking algorithm. Hidden Markov models are assigned to the segments in order to learn and recognize the partial movement within each segment. A Bayesian network fuses the HMMs in order to perceive the movement of the two hands as a single entity.
Keyword(s): Digital video; belief networks; computer vision; gesture recognition; hidden Markov models; human computer interaction; image segmentation
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Shamaie, Atid and Sutherland, Alistair (2004) Bayesian fusion of hidden Markov models for understanding bimanual movements. In: FGR 2004 - 6th IEEE International Conference on Automatic Face and Gesture Recognition, 17-19 May 2004, Seoul, Korea.
Publisher(s): Institute of Electrical and Electronics Engineers
File Format(s): application/pdf
Related Link(s):,
First Indexed: 2009-11-05 02:00:27 Last Updated: 2019-02-09 07:04:50