Name
Abstract | ||
|---|---|---|
Hand gesture recognition aims to recognize the meaningful expressions of hand motion. It is widely used in information visualization, robotics, sign language understanding, medicine and healthcare. Some methods have been proposed for hand gesture recognition. But no single algorithm can handle all kinds of situations, because of the complex environment. In this study, we propose a hybrid method for hand gesture recognition, which extends our previous work on a gesture recognition method based on concept learning by the addition of an association learning process. We use association learning to reveal the frequent patterns in gesture sequences, and then use such patterns to help recognize incomplete gesture sequences. Experiments show the use of association learning does indeed improve recognition accuracy. Experiments also show the hybrid method is comparable to two state of the art methods (HMMs and DTW) for hand gesture recognition, but outperforms them in the larger datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IE.2012.30 | Intelligent Environments |
Keywords | Field | DocType |
hand gesture recognition,hybrid method,recognition accuracy,complex environment,art method,hand motion,gesture sequence,incomplete gesture sequence,gesture recognition method,association learning,association rules,accuracy,gesture recognition,hidden markov models,classification algorithms | Information visualization,Pattern recognition,Gesture,Computer science,Gesture recognition,Speech recognition,Association rule learning,Sign language,Sketch recognition,Artificial intelligence,Hidden Markov model,Statistical classification | Conference |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huang, Yu | 1 | 5 | 4.54 |
| Dorothy. Monekosso | 2 | 14 | 1.74 |
| Hui Wang | 3 | 340 | 59.46 |
| Juan Carlos Augusto | 4 | 1344 | 145.59 |