Name
Abstract | ||
|---|---|---|
We describe a new approach to extracting layered representations from image sequences based on moving object graphs(MOGs). A MOG is a form of re- gion adjacency graph which links together local motion segmentations corre- sponding to distinct moving regions in the scene, typically either foreground objects or the background. The local motion segmentations are obtained by fusing colour segmentations with block motion estimates and the MOGs link segmentations with consistent spatial and motion properties. Linking MOGs across frames then allows temporal consistency to be imposed and layers to be extracted. The approach provides a flexible framework within which to combine local and global constraints both spatially and temporally, enabling robust motion segmentation and layer extraction. Results of experiments on real sequences illustrate that the approach is effective. |
Year | Venue | Keywords |
|---|---|---|
2001 | BMVC | motion estimation |
Field | DocType | Citations |
Adjacency list,Computer vision,Graph,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Motion estimation,Temporal consistency | Conference | 1 |
PageRank | References | Authors |
0.36 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Tweed | 1 | 60 | 7.71 |
| Andrew Calway | 2 | 645 | 54.66 |