Name
Abstract | ||
|---|---|---|
We propose a novel robust estimation algorithm—the generalized projection-based M-estimator (gpbM), which does not require the user to specify any scale parameters. The algorithm is general and can handle heteroscedastic data with multiple linear constraints for single and multicarrier problems. The gpbM has three distinct stages—scale estimation, robust model estimation, and inlier/outlier dichotomy. In contrast, in its predecessor pbM, each model hypotheses was associated with a different scale estimate. For data containing multiple inlier structures with generally different noise covariances, the estimator iteratively determines one structure at a time. The model estimation can be further optimized by using Grassmann manifold theory. We present several homoscedastic and heteroscedastic synthetic and real-world computer vision problems with single and multiple carriers. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TPAMI.2012.52 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
scale parameter,novel robust estimation algorithm,different scale estimate,multiple inlier structure,scale estimation,model estimation,generalized projection-based m-estimator,model hypothesis,robust model estimation,multiple carrier,multiple linear constraint,estimation theory,computational modeling,heteroscedasticity,computer vision,estimation,noise measurement,robustness,covariance matrix,ransac | Kernel (linear algebra),M-estimator,Pattern recognition,Computer science,RANSAC,Outlier,Robustness (computer science),Artificial intelligence,Covariance matrix,Estimation theory,Estimator | Journal |
Volume | Issue | ISSN |
34 | 12 | 1939-3539 |
Citations | PageRank | References |
26 | 0.95 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sushil Mittal | 1 | 89 | 5.45 |
| Saket Anand | 2 | 87 | 9.36 |
| Peter Meer | 3 | 9531 | 835.61 |