Name
Abstract | ||
|---|---|---|
UAVs in both civil and defense aviation have found numerous application areas in the last decade which resulted in sophisticated systems that extracts high level information utilizing the visual data from UAVs. Camera auto-calibration is always the first step if the intrinsic parameters of the cameras are not available. However, this process is not trivial as the aerial imagery mostly contains planar scenes which constitute a degenerate condition for conventional methods. In this paper, we propose a hybrid approach which incorporates circular point and camera position constraints as a single optimization term to automatically calibrate the camera of the UAV on planar scenes. The experimental results show that our proposed hybrid method is more robust and accurate than the conventional counterparts. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/AIPR.2017.8457959 | 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) |
Keywords | Field | DocType |
Auto-calibration,Planar Scene Reconstruction,Metadata Recovery,Aerial Imagery | Iterative reconstruction,Metadata,Computer vision,Computer science,Camera auto-calibration,Robustness (computer science),Planar,Global Positioning System,Artificial intelligence,Aerial imagery,Calibration | Conference |
ISSN | ISBN | Citations |
1550-5219 | 978-1-5386-1236-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdullah Akay | 1 | 0 | 0.68 |
| Hadi Aliakbarpour | 2 | 90 | 14.79 |
| Kannappan Palaniappan | 3 | 920 | 75.13 |
| Guna Seetharaman | 4 | 584 | 44.59 |