Name
Abstract | ||
|---|---|---|
We introduce a robust estimator called generalized projection based M-estimator (gpbM) which does not require the user to specify any scale parameters. For multiple inlier structures, with different noise covariances, the estimator iteratively determines one inlier structure at a time. Unlike pbM, where the scale of the inlier noise is estimated simultaneously with the model parameters, gpbM has three distinct stages-scale estimation, robust model estimation and inlier/outlier dichotomy. We evaluate our performance on challenging synthetic data, face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset. Results of state-of-the-art methods are presented for comparison. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CVPR.2011.5995514 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
estimation theory,face recognition,image denoising,image segmentation,Hopkins155 dataset,Yale Face Database B,face image clustering,generalized projection based M-estimator,gpbM,inlier/outlier dichotomy,multi body projective motion segmentation problem,multiple inlier structures,noise covariances,robust model estimation,stages scale estimation | M-estimator,Pattern recognition,Computer science,Outlier,Robust statistics,Image segmentation,Robustness (computer science),Artificial intelligence,Estimation theory,Cluster analysis,Estimator | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4577-0394-2 | 12 | 0.53 |
References | Authors | |
24 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sushil Mittal | 1 | 89 | 5.45 |
| Saket Anand | 2 | 87 | 9.36 |
| Peter Meer | 3 | 9531 | 835.61 |