Name
Abstract | ||
|---|---|---|
We consider a recent parametric level-set segmentation ap- proach where the implicit interface is the zero level of a con- tinuous function expanded onto compactly supported radial basis functions, defined by their centers, coefficients and sup- ports. We propose to introduce prior knowledge of the shape to be recovered by placing the centers quasi-uniformly over an uncertainty area. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICIP.2007.4379136 | Image Processing, 2007. ICIP 2007. IEEE International Conference |
Keywords | Field | DocType |
image segmentation,radial basis function networks,image segmentation,model based parametric level-set framework,radial basis functions collocation method,Compactly Supported Radial Basis Functions,Model-Based,Parametric Level-Set,Segmentation | Computer vision,Continuous function,Radial basis function network,Scale-space segmentation,Radial basis function,Computer science,Segmentation-based object categorization,Image segmentation,Parametric statistics,Artificial intelligence,Collocation | Conference |
Volume | ISSN | ISBN |
2 | 1522-4880 E-ISBN : 978-1-4244-1437-6 | 978-1-4244-1437-6 |
Citations | PageRank | References |
2 | 0.44 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gelas, A. | 1 | 2 | 0.44 |
| Schaerer, J. | 2 | 3 | 0.82 |
| Olivier Bernard | 3 | 9 | 2.14 |
| Denis Friboulet | 4 | 403 | 32.65 |