Name
Abstract | ||
|---|---|---|
Scene understanding is still an important challenge in robotics. Nevertheless scene recognition involves determining when an image is good enough to represent the scene and therefore it can be used for classification. Most research on scene recognition involves working with sets of images which have been acquired using a predefined sampling rate, nevertheless, this means working with very noisy and redundant sets of images. In this paper we analyse different alternatives to automatically select images according to amount of information they provide and how representative they are. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-19390-8_58 | PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015) |
Keywords | Field | DocType |
Scene recognition, Sampling, Canonical views | Computer vision,Pattern recognition,Computer science,Sampling (signal processing),Artificial intelligence,Sampling (statistics),Robot,Robotics | Conference |
Volume | ISSN | Citations |
9117 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. Santos-Saavedra | 1 | 7 | 1.88 |
| Xose Manuel Pardo | 2 | 42 | 5.30 |
| Roberto Iglesias | 3 | 25 | 7.68 |