Name
Abstract | ||
|---|---|---|
Regression-based schemes have proven effective for locating landmarks on images. Most previous approaches either predict the positions of all points simultaneously, or use regressors that predict individual points combined with a global shape constraint. The former can be efficient, but such models tend to be less robust. Conversely, Random Forest (RF) voting methods for individual points have been shown to be robust and accurate, but can lead to very large models. We explore the continuum between these two approaches by training RF regressors to predict subsets of points. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.07.009 | Procedia Computer Science |
Keywords | Field | DocType |
Random Forests,Constrained Local Models,Landmark Annotation,DXA Imaging | Model matching,Data mining,Regression,Voting,Computer science,Artificial intelligence,Random forest,Machine learning | Conference |
Volume | ISSN | Citations |
90 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul A. Bromiley | 1 | 172 | 10.26 |
| Claudia Lindner | 2 | 248 | 12.67 |
| Jessie Thomson | 3 | 0 | 0.34 |
| M. Wrigley | 4 | 0 | 0.34 |
| Timothy F. Cootes | 5 | 4358 | 579.15 |