Name
Abstract | ||
|---|---|---|
We propose a novel method to accurately estimate the global position of a moving car using an omnidirectional camera and untextured 3D city models. The camera is ori- ented upwards to capture images of the immediate skyline, which is generally unique and serves as a fingerprint for a specific location in a city. Our goal is to estimate global po- sition by matching skylines extracted from omni-directional images to skyline segments from coarse 3D city models. Our contributions include a sky segmentation algorithm for omni-directional images using graph cuts and a novel ap- proach for matching omni-image skylines to 3D models. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCVW.2009.5457723 | international conference on computer vision |
Keywords | Field | DocType |
cameras,feature extraction,geographic information systems,graph theory,image matching,image sensors,image texture,geolocalization,graph cuts,immediate skyline,matching skyline extraction,omni-images,omnidirectional camera,untextured 3D city models | Omnidirectional camera,Skyline,Cut,Computer vision,Pattern recognition,Image texture,Segmentation,Computer science,Feature extraction,Pixel,Artificial intelligence,3D city models | Conference |
Volume | Issue | Citations |
2009 | 1 | 21 |
PageRank | References | Authors |
0.92 | 30 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Ramalingam | 1 | 686 | 37.32 |
| Sofien Bouaziz | 2 | 934 | 35.79 |
| Peter Sturm | 3 | 2696 | 206.38 |
| Matthew Brand | 4 | 1204 | 82.37 |