Name
Abstract | ||
|---|---|---|
In this article, we propose a new method of image stitching that computes, in a robust manner, the transformation model applied to creating a panorama that is close to reality. The random selection of matching points used in existing methods, using Random Sample Consensus (RANSAC) or the threshold of the execution process (iteration number) cannot generally provide sufficient precision. Our approach, in this regard, comes to solve this problem. The calculation of the transformation model is based on the VORONOI diagram that divides images into regions to be used in the matching instead of control points. In this case, the transformation estimation will be based on the regions seeds that provide the best correlation score. Among the advantages of our method is solving problems related to outliers that can, in existing methods, affect the reliability of the mosaic. The results obtained are satisfactory in terms of stability, quality, execution time and reduction of the computational complexity. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11042-016-3478-z | Multimedia Tools Appl. |
Keywords | Field | DocType |
Panorama, Mosaic, Voronoi, Sift, Geometric solution, Ransac, Stitching | Computer vision,Scale-invariant feature transform,Image stitching,Pattern recognition,Panorama,RANSAC,Computer science,Outlier,Artificial intelligence,Sampling (statistics),Voronoi diagram,Computational complexity theory | Journal |
Volume | Issue | ISSN |
76 | 6 | 1573-7721 |
Citations | PageRank | References |
4 | 0.42 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Laraqui | 1 | 4 | 0.42 |
| A. Baataoui | 2 | 5 | 0.78 |
| Abderrahim Saaidi | 3 | 44 | 10.56 |
| A. O. Jarrar | 4 | 4 | 0.42 |
| Med Masrar | 5 | 4 | 0.42 |
| Khalid Satori | 6 | 42 | 16.75 |