Name
Abstract | ||
|---|---|---|
We address the problem of segmenting a sequence of images of natural scenes into disjoint regions that are characterized by constant spatio-temporal statistics. We model the spatio-temporal dynamics in each region by Gauss-Markov models, and infer the model parameters as well as the boundary of the regions in a variational optimization framework. Numerical results demonstrate that - in contrast to purely texture-based segmentation schemes - our method is effective in segmenting regions that differ in their dynamics even when spatial statistics are identical. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICCV.2003.1238632 | Nice, France |
Keywords | Field | DocType |
Gaussian processes,Markov processes,computer vision,image segmentation,image texture,natural scenes,optimisation,variational techniques,Gauss-Markov models,disjoint regions,dynamic texture segmentation,image sequence,model parameters,natural scenes,numerical results,spatial statistics,spatiotemporal dynamics,spatiotemporal statistics,variational optimization framework | Spatial analysis,Computer vision,Markov process,Disjoint sets,Pattern recognition,Image texture,Computer science,Range segmentation,Segmentation,Image segmentation,Gaussian process,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
0-7695-1950-4 | 83 | 3.71 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianfranco Doretto | 1 | 1026 | 78.58 |
| Daniel Cremers | 2 | 8236 | 396.86 |
| Paolo Favaro | 3 | 1236 | 71.44 |
| Stefano Soatto | 4 | 4967 | 350.34 |