Name
Title | ||
|---|---|---|
Dynamic Block-Based Parameter Estimation for MRF Classification of High-Resolution Images. |
Abstract | ||
|---|---|---|
A Markov random field is a graphical model that is commonly used to combine spectral information and spatial context into image classification problems. The contributions of the spatial versus spectral energies are typically defined by using a smoothing parameter, which is often set empirically. We propose a new framework to estimate the smoothing parameter. For this purpose, we introduce the new ... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LGRS.2014.2305913 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Smoothing methods,Image edge detection,Accuracy,Support vector machines,Remote sensing,Parameter estimation,Educational institutions | Computer vision,Canny edge detector,Pattern recognition,Markov random field,Multispectral image,Hyperspectral imaging,Smoothing,Artificial intelligence,Estimation theory,Graphical model,Contextual image classification,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 10 | 1545-598X |
Citations | PageRank | References |
5 | 0.49 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hossein Aghighi | 1 | 12 | 2.82 |
| John Trinder | 2 | 101 | 15.24 |
| Yuliya Tarabalka | 3 | 907 | 47.12 |
| Samsung Lim | 4 | 68 | 12.02 |