Name
Abstract | ||
|---|---|---|
Robust estimation of model parameters in the presence of outliers is a key problem in computer vision. RANSAC inspired techniques are widely used in this context, although their application might be limited due to the need of a priori knowledge on the inlier noise level. We propose a new approach for jointly optimizing over model parameters and the inlier noise level based on the likelihood ratio test. This allows control over the type I error incurred. We also propose an early bailout strategy for efficiency. Tests on both synthetic and real data show that our method outperforms the state-of-the-art in a fraction of the time. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCV.2015.263 | ICCV |
Field | DocType | Volume |
Computer vision,Pattern recognition,Likelihood-ratio test,Computer science,RANSAC,Noise level,A priori and a posteriori,Outlier,Artificial intelligence,Type I and type II errors,Machine learning | Conference | 2015 |
Issue | ISSN | Citations |
1 | 1550-5499 | 2 |
PageRank | References | Authors |
0.36 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Cohen | 1 | 131 | 5.53 |
| Christopher Zach | 2 | 1457 | 84.01 |