Name
Abstract | ||
|---|---|---|
. Learning is a critical research field for autonomous computer vision systems. Itcan bring solutions to the knowledge acquisition bottleneck of image understanding systems.Recent developments of machine learning for computer vision are reported in this paper. Wedescribe several different approaches for learning at different levels of the imageunderstanding process, including learning 2-D shape models, learning strategic knowledgefor optimizing model matching, learning for adaptative... |
Year | Venue | Keywords |
|---|---|---|
1994 | IJPRAI | object recognition,vision system,computer vision,autonomic computing,machine learning |
Field | DocType | Volume |
Robot learning,Algorithmic learning theory,Instance-based learning,Active learning (machine learning),Inductive transfer,Pattern recognition,Computer science,Artificial intelligence,Computational learning theory,Machine learning,Knowledge acquisition,Cognitive neuroscience of visual object recognition | Journal | 8 |
Issue | Citations | PageRank |
1 | 5 | 0.84 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yves Kodratoff | 1 | 581 | 172.25 |
| Stéphane Moscatelli | 2 | 5 | 0.84 |