Name
Abstract | ||
|---|---|---|
One of the most general and acknowledged models for background statistics characterization is the family of elliptically symmetric distributions. They account for heterogeneity and non-Gaussianity of real data. Today, although nonGaussian models are assumed for background modeling and design of detectors, the parameters estimation is usually performed using classical Gaussian-based estimators. Thi... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LSP.2017.2763784 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Robustness,Covariance matrices,Detectors,Maximum likelihood estimation,Object detection,Parameter estimation | Statistical mean,Robustness (computer science),Artificial intelligence,Estimation theory,Detector,Object detection,Pattern recognition,Algorithm,Gaussian,Constant false alarm rate,Machine learning,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
24 | 12 | 1070-9908 |
Citations | PageRank | References |
3 | 0.39 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joana Frontera-Pons | 1 | 25 | 4.07 |
| Jean Philippe Ovarlez | 2 | 190 | 25.11 |
| Frédéric Pascal | 3 | 128 | 16.30 |