Name
Abstract | ||
|---|---|---|
In this work, we propose a contour and region detector for video data that exploits motion cues and distinguishes occlusion boundaries from internal boundaries based on optical flow. This detector outperforms the state-of-the-art on the benchmark of Stein and Hebert, improving average precision from .58 to .72. Moreover, the optical flow on and near occlusion boundaries allows us to assign a depth ordering to the adjacent regions. To evaluate performance on this edge-based figure/ground labeling task, we introduce a new video dataset that we believe will support further research in the field by allowing quantitative comparison of computational models for occlusion boundary detection, depth ordering and segmentation in video sequences. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CVPR.2011.5995364 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
computer graphics,image sequences,computational models,contour detector,occlusion boundary detection,optical flow,region detector,video data,video dataset,video sequences | Computer vision,Occlusion,Segmentation,Computer science,Figure–ground,Computational model,Boundary detection,Artificial intelligence,Detector,Computer graphics,Optical flow | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4577-0394-2 | 61 | 1.81 |
References | Authors | |
25 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sundberg, P. | 1 | 61 | 1.81 |
| Thomas Brox | 2 | 7866 | 327.52 |
| Michael Maire | 3 | 4630 | 231.57 |
| Pablo Arbelaez | 4 | 3626 | 173.00 |