Name
Abstract | ||
|---|---|---|
This paper presents a new object-based segmentation technique which exploits a large temporal context in order to get coherent and robust segmentation results. The segmentation process is seen as a problem of minimization of an energy function. This energy function takes into account a data attach term and spatial and temporal regularization terms. The proposed technique used to minimize this energy function is decomposed into three main steps: 1) definition of a technique for retrieving potential objects (referenced as seed extraction), 2) motion estimation for each seed, and 3) final classification performed by minimizing the energy function using a clustering-like technique. The proposed segmentation technique has been validated on real video sequences. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICIP.2002.1039895 | ICIP (2) |
Keywords | DocType | Volume |
feature extraction,tracking,merging,minimisation,image processing,data mining,clustering,motion estimation,robustness,context modeling,image segmentation,image classification | Conference | 2 |
ISSN | Citations | PageRank |
1522-4880 | 2 | 0.37 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marc Chaumont | 1 | 172 | 20.40 |
| Stéphane Pateux | 2 | 255 | 33.64 |
| Henri Nicolas | 3 | 49 | 7.64 |