Name
Abstract | ||
|---|---|---|
We propose a binary Markov random field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We construct the model by discretizing the `gas of circles' phase field model in a principled way, thereby creating an `equivalent'MRF. The behaviour of the resulting MRF model is analyzed, and the performance of the new model is demonstrated on various synthetic images as well as on the problem of tree crown detection in aerial images. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5413472 | Image Processing |
Keywords | Field | DocType |
Markov processes,feature extraction,image segmentation,Markov random field model,aerial image detection,gas of circles phase field model,image segmentation,near-circular shapes extraction,synthetic images,Markov random field,segmentation,shape prior | Simulated annealing,Computer vision,Discretization,Markov process,Pattern recognition,Computer science,Markov random field,Segmentation,Markov model,Feature extraction,Image segmentation,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 2 |
PageRank | References | Authors |
0.37 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tamas Blaskovics | 1 | 2 | 0.37 |
| Zoltan Kato | 2 | 265 | 28.28 |
| Ian Jermyn | 3 | 894 | 79.47 |