Name
Title | ||
|---|---|---|
Synthetic aperture radar image segmentation by a detail preserving Markov random field approach |
Abstract | ||
|---|---|---|
A multichannel image segmentation method is imposed that utilizes Markov random fields (MRFs) with adaptive neighborhood (AN) systems. Bayesian inference is applied to realize the combination of evidence from different knowledge sources. In such a way, optimization of the shape of a neighborhood system is achieved by following a criterion that makes use of the Markovian property exploiting the loc... |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/36.602527 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Synthetic aperture radar,Image segmentation,Markov random fields,Shape,Bayesian methods,Remote sensing,Roads,Image processing,Algorithm design and analysis,Adaptive systems | Computer vision,Markov process,Scale-space segmentation,Segmentation,Computer science,Markov random field,Synthetic aperture radar,Remote sensing,Markov chain,Segmentation-based object categorization,Image segmentation,Artificial intelligence | Journal |
Volume | Issue | ISSN |
35 | 4 | 0196-2892 |
Citations | PageRank | References |
45 | 5.43 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. C. Smits | 1 | 115 | 17.06 |
| S. G. Dellepiane | 2 | 75 | 9.19 |