Name
Abstract | ||
|---|---|---|
The core step of video stabilization is to estimate global motion from locally extracted motion clues. Outlier motion clues are generated from moving objects in image sequence, which cause incorrect global motion estimates. Random sample consensus (RANSAC) is popularly used to solve such outlier problem. RANSAC needs to tune parameters with respect to the given motion clues, so it sometimes fail when outlier clues are increased than before. Adaptive RANSAC is proposed to solve this problem, which is based on maximum likelihood sample consensus (MLESAC). It estimates the ratio of outliers through expectation maximization (EM), which entails the necessary number of iteration for each frame. The adaptation sustains high accuracy in varying ratio of outliers and faster than RANSAC when fewer iteration is enough. Performance of adaptive RANSAC is verified in experiments using four images sequences. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IROS.2009.5354240 | IROS |
Keywords | Field | DocType |
video signal processing,expectation-maximisation algorithm,motion clue,robust video stabilization,global motion estimation,outlier clue,global motion,maximum likelihood estimation,adaptive ransac,expectation maximization,outlier motion,feature extraction,incorrect global motion estimate,fewer iteration,image sequence,image sequences,outlier motion clue,maximum likelihood sample consensus,adaptive random sample consensus,random sample consensus,iterative methods,outlier problem,computational modeling,optical filters,data models,maximum likelihood,robustness,random sampling,kalman filters,tracking | Computer vision,Pattern recognition,RANSAC,Computer science,Expectation–maximization algorithm,Iterative method,Image stabilization,Outlier,Robustness (computer science),Feature extraction,Kalman filter,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-1-4244-3804-4 | 2 | 0.40 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunglok Choi | 1 | 324 | 15.25 |
| Taemn Kim | 2 | 382 | 28.18 |
| Wonpil Yu | 3 | 330 | 31.10 |