Name
Title | ||
|---|---|---|
Non-parametric Bayesian framework for detection of object configurations with large intensity dynamics in highly noisy hyperspectral data |
Abstract | ||
|---|---|---|
In this study, a method that aims at detecting small and faint objects in noisy hyperspectral astrophysical images is presented. The particularity of the hyperspectral images that we are interested in is the high dynamics between object intensities. Detection of the smallest and faintest objects is challenging, because their signal-to-noise ratio is low, and if the brightest objects are not well reconstructed, their residuals can be more energetic than faint objects. This paper proposes a marked point process within a nonparametric Bayesian framework for the detection of galaxies in hyperspectral data. The efficiency of the method is demonstrated on synthetic images, and it provides good results for very faint objects in quasi-real astrophysical hyperspectral data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853926 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
Bayes methods,astronomy,object detection,signal processing,faint object detection,large intensity dynamics,noisy hyperspectral astrophysical images,noisy hyperspectral data,nonparametric Bayesian framework,object configurations,quasi-real astrophysical hyperspectral data,signal-to-noise ratio,synthetic images,Detection,hyperspectral data,marked point process,nonparametric Bayesian models | Computer vision,Pattern recognition,Computer science,Nonparametric bayesian,Nonparametric statistics,Hyperspectral imaging,Artificial intelligence,Marked point process,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.37 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Celine Meillier | 1 | 4 | 0.84 |
| Florent Chatelain | 2 | 7 | 2.66 |
| Olivier J. j. Michel | 3 | 232 | 23.78 |
| Hacheme Ayasso | 4 | 33 | 5.13 |