Name
Title | ||
|---|---|---|
Recognizing Submerged Materials with Fluorescence Lidar without Knowledge of Environmental Conditions |
Abstract | ||
|---|---|---|
This work presents a submerged object recognition method with fluorescence LIDAR that can be applied when no a priori information about environmental conditions is available. Whereas conventional methods require the availability of either LIDAR measurements of water samples or accurate knowledge about environmental conditions, the approach investigated here remove such assumptions. Experimental results on real data acquired in laboratory show the potential of the approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IGARSS.2019.8898786 | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
Fluorescence LIDAR,underwater material discriminability,target recognition,LIDAR simulation,invariance | Computer vision,Computer science,A priori and a posteriori,Remote sensing,Lidar,Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2153-6996 | 978-1-5386-9155-7 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefania Matteoli | 1 | 152 | 18.05 |
| Giovanni Corsini | 2 | 299 | 40.26 |
| Marco Diani | 3 | 261 | 30.99 |