Name
Abstract | ||
|---|---|---|
Abstract An approach is presented to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method,is based on the assumption,that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, starting and ending in a rest position and governed by a high level structure controlling the temporal sequence. It is shown,that the generating processes for the atomic components,and derived gesture models can be described by a mixture of Gaussian in their respective component,and gesture space. Mixture components,modelling atomic components,and gestures respectively are determined using a standard EM approach, while the determination of the number,of mixture components,and therefore the number,of atomic components and gestures is based on an information criterion, the Minimum,Description Length (MDL). |
Year | Venue | Keywords |
|---|---|---|
2001 | BMVC | mixture of gaussians,minimum description length |
Field | DocType | Citations |
Computer vision,Data-driven,Pattern recognition,Computer science,Minimum description length,Speech recognition,Artificial intelligence | Conference | 9 |
PageRank | References | Authors |
1.07 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Walter | 1 | 111 | 10.36 |
| Alexandra Psarrou | 2 | 199 | 27.14 |
| Shaogang Gong | 3 | 7941 | 498.04 |