Name
Abstract | ||
|---|---|---|
Estimation of topography and fusion of data acquired over multiple resolutions have been extensively studied over the years. The standard MKS (Multiscale Kalman Smoother) algorithm embedded with a single stochastic model parameterized using power spectra matching methods have been found to give suboptimal performance in estimating non-stationary topographic variations. In this work, multiple models are regulated by a mixture-of-experts (MOE) network to adaptively fuse the estimates. A Fractal based approach was employed to segment the data and parameterize the multiple models for better performance. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICIP.2005.1529861 | 2005 International Conference on Image Processing (ICIP), Vols 1-5 |
Keywords | Field | DocType |
multiscale estimation, firactal modeling, multimodel MKS, expectation-maximization | Parameterized complexity,Pattern recognition,Topographic map,Computer science,Fractal,Stochastic process,Kalman filter,Image segmentation,Stochastic modelling,Artificial intelligence,Fuse (electrical) | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karthik Nagarajan | 1 | 77 | 7.23 |
| Clint Slatton | 2 | 79 | 18.56 |