Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/AFGR.2004.1301599 | FGR |
Keywords | Field | DocType |
hidden markov models,bayesian fusion,fusing hidden markov models,bayesian network,hidden markov model,computer vision,human-computer interaction,current research,partial movement,single entity,bimanual movement,body gesture,bayesian methods,tracking,image segmentation,kalman filters,human computer interaction,fuses,spectrum,layout,face detection,gesture recognition | Computer vision,Gesture,Computer science,Gesture recognition,Image segmentation,Bayesian programming,Bayesian network,Artificial intelligence,Graphical model,Hidden Markov model,Dynamic Bayesian network | Conference |
ISBN | Citations | PageRank |
0-7695-2122-3 | 3 | 0.38 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atid Shamaie | 1 | 6 | 0.88 |
| Alistair Sutherland | 2 | 101 | 14.36 |