Name
Abstract | ||
|---|---|---|
We examine in detail some properties of gesture recognition models which utilize a reduced number of parameters and lower algorithmic complexity compared to traditional hidden Markov models. We show that the reduced parameter models are comparable to standard HMM-based gesture recognition models in their ability to effectively model gestures, and in some cases superior when training data is limited. We also show that in order to effectively differentiate similar gestures, a gesture recognition model must utilize a large number of states, a scenario which can only be adequately handled by reducer parameter methods to maintain real-time speeds. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/AFGR.2008.4813425 | 2008 8TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2008), VOLS 1 AND 2 |
Keywords | Field | DocType |
probability density function,data models,data mining,hidden markov models,gesture recognition,hidden markov model,real time | Data modeling,Pattern recognition,Gesture,Computer science,Gesture recognition,Speech recognition,Artificial intelligence,Reducer,Hidden Markov model,Algorithmic complexity,Probability density function,Parameter reduction | Conference |
ISSN | Citations | PageRank |
2326-5396 | 7 | 0.78 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stjepan Rajko | 1 | 154 | 14.22 |
| Gang Qian | 2 | 784 | 63.77 |