Name
Abstract | ||
|---|---|---|
A novel video object segmentation method is proposed which aims at combining color and motion information. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where a classical Markov random field (MRF) image segmentation model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model, called combined layer, which interacts with each feature layer and provides the segmentation based on the combination of different features. Unlike previous methods, our approach doesn’t assume motion boundaries being part of spatial ones. Therefore a very important property of the proposed method is the ability to detect boundaries that are visible only in the motion feature as well as those visible only in the color one. The method is validated on synthetic and real video sequences. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11612704_95 | ACCV |
Keywords | Field | DocType |
special layer,video object segmentation,image segmentation model,own layer,different feature,motion feature,feature layer,motion boundary,combined mrf model,corresponding feature,multi-layer mrf model,combined layer,image segmentation | Computer vision,Multi layer,Scale-space segmentation,Markov process,Pattern recognition,Computer science,Segmentation,Markov random field,Image processing,Image segmentation,Artificial intelligence,Color image | Conference |
Volume | ISSN | ISBN |
3852 | 0302-9743 | 3-540-31244-7 |
Citations | PageRank | References |
4 | 0.41 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zoltan Kato | 1 | 265 | 28.28 |
| T.-C. Pong | 2 | 942 | 106.95 |