Name
Abstract | ||
|---|---|---|
A large number of computer vision applications rely on camera calibration. Camera self-calibration which only depends on the relationship between corresponding points of a pair of images draws much attention for its simplicity. Almost all the camera self-calibration methods rely on the solution of Kruppa equations which are difficult to be directly solved. The state-of-the-art self-calibration algorithms usually convert the solution of these equations to non-linear optimization problem, traditional optimization methods usually have the drawback of convergent to local extreme. Artificial immune system (AIS) has the ability to fast convergent to global extreme. To address this problem, we proposed an artificial immune system based method which can fast convergent to the global optimization solutions. We demonstrate the performance of the proposed method with synthetic and real data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/VCIP.2013.6706377 | VCIP |
Keywords | Field | DocType |
csa,ais,calibration,kruppa equations,camera self-calibration,nonlinear programming,camera self-calibration method,kruppa equation,clonal selection algorithm,computer vision application,nonlinear optimization problem,cameras,computer vision,fundamental matrix,artificial immune systems,artificial immune system | Computer vision,Artificial immune system,Global optimization,Computer science,Nonlinear programming,Camera auto-calibration,Image processing,Camera resectioning,Visual communication,Artificial intelligence,Optimization problem | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4799-0288-0 |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Chuan Geng | 1 | 3 | 1.07 |
| Shao-Zi Li | 2 | 76 | 6.46 |
| Song-zhi Su | 3 | 61 | 8.53 |
| Donglin Cao | 4 | 142 | 17.21 |
| Yunqi Lei | 5 | 5 | 1.43 |
| Rongrong Ji | 6 | 3616 | 189.98 |