| dc.creator |
Ramisa, Arnau |
|
| dc.creator |
Lopez de Mantaras, Ramon |
|
| dc.creator |
Aldavert, David |
|
| dc.creator |
Toledo, Ricardo |
|
| dc.date |
2008-03-19T11:29:25Z |
|
| dc.date |
2008-03-19T11:29:25Z |
|
| dc.date |
2007 |
|
| dc.date.accessioned |
2017-01-31T01:00:51Z |
|
| dc.date.available |
2017-01-31T01:00:51Z |
|
| dc.identifier |
ICINCO-07 4th International Conference on Informatics in Control, Automation and Robotics, Angers France, 9-12 May, 2007. p. p.: 292-297 |
|
| dc.identifier |
http://hdl.handle.net/10261/3262 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3262 |
|
| dc.description |
Invariant (or covariant) image feature region detectors and descriptors are useful in visual robot navigation because they provide a fast and reliable way to extract relevant and discriminative information from an image and, at the same time, avoid the problems of changes in illumination or in point of view. Furthermore, complementary types of image features can be used simultaneously to extract even more information. However, this advantage always entails the cost of more processing time and sometimes, if not used wisely, the performance can be even worse. In this paper we present the results of a comparison between various combinations of region detectors and descriptors. The test performed consists in computing the essential matrix between panoramic images using correspondences established with these methods. Different combinations of region detectors and descriptors are evaluated and validated using ground truth data. The results will help us to find the best combination to use it in an autonomous robot navigation system. |
|
| dc.description |
This work has been partially
supported by the FI grant from the Generalitat
de Catalunya, the European Social Fund and the
MID-CBR project grant TIN2006-15140-C03-01 and
FEDER funds. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
266541 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.rights |
openAccess |
|
| dc.subject |
Artificial Intelligence |
|
| dc.subject |
Affine covariant regions |
|
| dc.subject |
Local descriptors |
|
| dc.subject |
Interest points |
|
| dc.subject |
Matching |
|
| dc.subject |
Robot navigation |
|
| dc.subject |
Panoramic images |
|
| dc.title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
|
| dc.type |
Comunicación de congreso |
|