Name
Abstract | ||
|---|---|---|
In this study, we present a novel, robust, and accurate method to register multiple images captured by an unmanned aerial vehicle (UAV) with modified camera matrixes, and then stitch UAV images to a seamless one. Firstly, the camera parameters, camera poses and sparse 3D points are solved using feature correspondence and SEM. Secondly, all images are registered using sparse 3D point cloud and modified camera matrixes. Thirdly, the stitching plane is determined by performing a view selection. At last, all images are stitched using multi-band blending based on graph-cut. Experiments and comparisons demonstrate the accuracy and feasibility of our approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/RCAR.2016.7784091 | 2016 IEEE INTERNATIONAL CONFERENCE ON REAL-TIME COMPUTING AND ROBOTICS (IEEE RCAR) |
Field | DocType | Citations |
Computer vision,Image stitching,Computer graphics (images),Matrix (mathematics),Computer science,Camera auto-calibration,Camera resectioning,Artificial intelligence,Camera matrix,Point cloud | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou Hang | 1 | 0 | 0.34 |
| Zhou Dongxiang | 2 | 148 | 13.50 |
| Peng Keju | 3 | 10 | 3.72 |
| Guo Ruibin | 4 | 0 | 0.34 |
| Liu YH | 5 | 1540 | 185.05 |