Name
Abstract | ||
|---|---|---|
Hand gesture is a useful modality of human interaction. In this paper, we propose an approach to recognition of space-time variable patterns of nonlinear arm movement and integration with other attributes to find the proper interpretation. At the encoding stage, we first extract the essential 2D trajectory from 3D arm movement by a plane fitting method. Pause information between the consecutive gestures is also modeled and integrated into the encoding. Codified information is then applied is a hidden Markov model (HMM) network which is responsible for segmentation and recognition of continuous arm movements. As a whole, three major attributes of hand gestures are processed in parallel and independently, followed by the inter-attribute communication for finding the proper interpretation. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1016/S0167-8655(96)00121-3 | Pattern Recognition Letters |
Keywords | Field | DocType |
nonlinear arm movement,network of hidden markov models,inter-attribute communication,dimension reduction,continuous hand gesture,hand gesture,human interaction,space time,hidden markov model | Computer vision,Dimensionality reduction,Nonlinear system,Segmentation,Computer science,Gesture,Gesture recognition,Speech recognition,Artificial intelligence,Hidden Markov model,Trajectory,Encoding (memory) | Journal |
Volume | Issue | ISSN |
18 | 1 | Pattern Recognition Letters |
Citations | PageRank | References |
12 | 0.98 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang-Hee Nam | 1 | 83 | 14.75 |
| KwangYun Wohn | 2 | 309 | 42.24 |