Name
Abstract | ||
|---|---|---|
We are interested in methods for building cognitive vision systems to understand activities of expert operators for our ActIPret System. Our approach to the gesture recognition required here is to learn the generic models and develop methods for contextual bias of the visual interpretation in the online system. The paper first introduces issues in the development of such flexible and robust gesture learning and recognition, with a brief discussion of related research. Second, the computational model for the Hidden Markov Model (HMM) is described and results with varying amounts of noise in the training and testing phases are given. Third, extensions of this work to allow both top-down bias in the contextual processing and bottom-up augmentation by moment to moment observation of the hand trajectory are described. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-24598-8_26 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
gesture recognition,bottom up,computer model,top down,hidden markov model | Computer science,Gesture,Visual interpretation,Gesture recognition,Speech recognition,Artificial intelligence,Operator (computer programming),Hidden Markov model,Trajectory,Cognitive vision | Conference |
Volume | ISSN | Citations |
2915 | 0302-9743 | 2 |
PageRank | References | Authors |
0.41 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kingsley Sage | 1 | 8 | 1.56 |
| jon howell | 2 | 585 | 39.63 |
| Hilary Buxton | 3 | 491 | 135.93 |