Name
Abstract | ||
|---|---|---|
This work deals with the problem of background-foreground segmentation in video scenes. We propose an approach that makes use of feature extraction and matching, robust estimation, background mosaic generation, mosaic back-projection, and segmentation. We make contributions in each of these steps. SIFT features are extracted from the video and then, features between consecutive frames are matched. There are mismatches as well as errors in the feature location. To surmount this, we use a robust approach that employs a modified version of the RANSAC algorithm and weighted total least squares. Knowing the global motion allows creating an initial foreground segmentation and the generation of a mosaic mainly using background data. The moving object extraction is done by combining a mean shift-based frame segmentation along with the information given by the error of the mosaic back-projection into each frame. |
Year | Venue | Keywords |
|---|---|---|
2007 | SIP | feature location,consecutive frame,feature extraction,sift feature,mean shift-based frame segmentation,mosaic-based figure-ground segmentation,background mosaic generation,static segmentation,background data,initial foreground segmentation,background-foreground segmentation,mosaic back-projection,robust estimator,mean shift,mosaic,sift,ransac |
Field | DocType | ISSN |
Computer vision,Scale-invariant feature transform,Scale-space segmentation,Pattern recognition,Segmentation,RANSAC,Computer science,Figure–ground,Feature extraction,Artificial intelligence,Mean-shift,Total least squares | Conference | 1482-7921 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joel Sole | 1 | 21 | 3.75 |
| Yu Huang | 2 | 15 | 1.24 |
| Joan Llach | 3 | 99 | 10.01 |