Name
Abstract | ||
|---|---|---|
In this paper, we present a new recognition system for the fast detection and classification of objects in spatial 3D data. The system consists of two main components: A biologically motivated attention system and a fast classifier. Input is provided by a 3D laser scanner, mounted on an autonomous mobile robot, that acquires illumination independent range and reflectance data. These are rendered into images and fed into the attention system that detects regions of potential interest. The classifier is applied only to a region of interest, yielding a significantly faster classification that requires only 30% of the time of an exhaustive search. Furthermore, both the attention and the classification system benefit from the fusion of the bi-modal data, considering more object properties for the detection of regions of interest and a lower false detection rate in classification. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30572-9_13 | WAPCV |
Keywords | Field | DocType |
object recognition,bi-modal data,lower false detection rate,classification system benefit,fast detection,new recognition system,potential interest,faster classification,attention system,reflectance data,biologically motivated attention system,visual attention,exhaustive search,region of interest | Computer vision,Laser scanning,Pattern recognition,Brute-force search,Property (programming),Artificial intelligence,Region of interest,Face detection,Classifier (linguistics),Geography,Mobile robot,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | ISBN |
3368 | 0302-9743 | 3-540-24421-2 |
Citations | PageRank | References |
8 | 1.08 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Frintrop | 1 | 695 | 42.88 |
| Andreas Nüchter | 2 | 1341 | 90.03 |
| Hartmut Surmann | 3 | 699 | 50.40 |