Computer Science – Learning
Scientific paper
Feb 2006
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006spie.6064..332g&link_type=abstract
Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning. Edited by Dougherty, Edward R.; Astola, Jaakko
Computer Science
Learning
10
Scientific paper
The GALEX mission of NASA, is collecting an unprecedent set of astronomical UV data in the far and the near UV range. The telescope measures the full sky in a continuous automatic scan. Knowing the attitude data, local images are simultaneously extracted and corrected for smearing and instrumental effects. Final UV images show, by far, a lower resolution than their visible counterpart. It originates blends, ambiguities and missidentifications of the astronomical sources. Our purpose is to deduce from the UV image the UV photometry of the visible objets through a bayesian approach, using the visible data (catalog and image) as the starting reference for the UV analysis. For the feasibility reasons as the deep field images are very large, a segmentation procedure has been defined to manage the analysis in a tractable form. The present paper discusses all these aspects and details the full method and performances.
Arnouts Stephane
Aymeric D.
Guillaume Mireille
Llebaria Antoine
Milliard Bruno
No associations
LandOfFree
Deblending of the UV photometry in GALEX deep surveys using optical priors in the visible wavelengths does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Deblending of the UV photometry in GALEX deep surveys using optical priors in the visible wavelengths, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Deblending of the UV photometry in GALEX deep surveys using optical priors in the visible wavelengths will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1165415