Name
Abstract | ||
|---|---|---|
GrabCut is a renowned algorithm for image segmentation. It exploits iteratively the combinatorial minimization of energy function as introduced in graph-cut methods, to achieve background foreground classification with fewer user's interaction. In this paper it is proposed to extend GrabCut to carry out segmentation on RGB-D point clouds, based both on appearance and geometrical criteria. It is shown that an hybrid GrabCut method combining RGB and D information, is more efficient than GrabCut based only on RGB or D images. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-02895-8_32 | ACIVS |
Keywords | Field | DocType |
segmentation,grabcut,max flow,graph cut | Cut,Computer vision,Pattern recognition,Computer science,Segmentation,GrabCut,Image segmentation,Minification,Artificial intelligence,RGB color model,Point cloud | Conference |
Volume | ISSN | Citations |
8192 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nizar Sallem | 1 | 1 | 1.04 |
| Michel Devy | 2 | 542 | 71.47 |