Name
Abstract | ||
|---|---|---|
We present a new image mosaicing technique that uses sequential aerial images captured from a camera and is capable of creating consistent large scale mosaics in real-time. To find the alignment of every new image, we use all the available images in the mosaic that have intersection with the new image instead of using only the previous one. To detect image intersections in an efficient manner, we utilize ‘Separating Axis Theorem’, a geometric tool from computer graphics which is used for collision detection. Moreover, after a certain number of images are added to the mosaic, a novel affine refinement procedure is carried out to increase global consistency. Finally, gain compensation and multi-band blending are optionally used as offline steps to compensate for photometric defects and seams caused by misregistrations. Proposed approach is tested on some public datasets and it is compared with two state-of-the-art algorithms. Results are promising and show the potential of our algorithm in various practical scenarios. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.robot.2014.07.010 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Image mosaicing,Bundle adjustment,MLESAC,Separating axis theorem,Affine refinement,Multi-band blending,Gain compensation | Affine transformation,Computer vision,Collision detection,Computer science,Simulation,Bundle adjustment,Artificial intelligence,Global consistency,Computer graphics | Journal |
Volume | Issue | ISSN |
62 | 12 | 0921-8890 |
Citations | PageRank | References |
11 | 0.51 | 33 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taygun Kekec | 1 | 11 | 0.85 |
| Alper Yildirim | 2 | 11 | 0.85 |
| Mustafa Ünel | 3 | 154 | 20.71 |