Name
Abstract | ||
|---|---|---|
In this paper we introduce a probabilistic approach to sup- port visual supervision and gesture recognition. Task knowledge is both of geometric and visual nature and it is encoded in parametric eigenspaces. Learning processes for compute modal subspaces (eigen- spaces) are the core of tracking and recognition of gestures and tasks. We describe the overall architecture of the system and detail learning processes and gesture design. Finally we show experimental results of tracking and recognition in block-world like assembling tasks and in general human gestures. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-64574-8_452 | IEA/AIE (Vol. 1) |
Keywords | Field | DocType |
. visual inspection,combined probabilistic framework,learning gestures,proba- bilistic constraints,learning,eigenmethods,gesture recognition,visual inspection | Computer science,Gesture,Expert system,Knowledge-based systems,Gesture recognition,Linear subspace,Parametric statistics,Artificial intelligence,Probabilistic logic,Machine learning,Modal | Conference |
ISBN | Citations | PageRank |
3-540-64574-8 | 0 | 0.34 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Escolano | 1 | 532 | 46.61 |
| Miguel Cazorla | 2 | 325 | 44.17 |
| Domingo Gallardo | 3 | 25 | 3.92 |
| Faraón Llorens | 4 | 5 | 3.78 |
| Rosana Satorre | 5 | 2 | 1.76 |
| R. Rizo | 6 | 51 | 14.90 |