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The Role of Computer Vision in Astronomy
Rob FergusNew York University
Overview
• Virtually all our knowledge about the universe derived from measurements of photons– Usually as images
• Big astronomy project is $50M-200M– But only 1-2% of this on software
• Just discovering techniques from computer vision & machine learning
Astrometry.net
Lang, Hogg, Mierle, Blanton & Roweis, [The Astronomical Journal, Vol. 137, 2010]
• Input: image of sky
• Output: – Absolute position – List of objects
• Geometric hashing(quads of stars)– Lamdan & Wolfson
[ICCV’88]
• Widely used by pros & amateurs
Removing Atmospheric Distortions
Hirsch, Harmeling, Sra & Schölkopf , [Astronomy & Astrophysics 2011]
• Ground-based telescopes look through atmosphere
• Blind (online) estimation of atmospheric distortion and true image
• Far better than “lucky” imaging (current approach)
Hirsch, Sra, Schölkopf & Harmeling, [CVPR 2010]
Exoplanet Imaging
• Want to image planets around other stars
• Need contrast ratio >1010
for Earth-like planets
• Diffraction in telescope– Light from star obscures planet
• Deconvolution problem– Big assistance from optical design
Crepp et al., [Astrophysical Journal, Vol. 729, 2011].
Galaxy / Star Classification
Star vs Galaxy
[Sloan Digital Sky Survey]
Galaxy / Star Classification
• Distinguish stars from galaxies
• SVM-based models– Smith et al. [A & A, Vol. 522, 2010]
• Generative model of galaxies– Lang et al. [In preparation]
Stars
Galaxies
Data Model
Future Directions
• UnifiedBayesian model
• Propagateuncertaintyfrom pixels
• Physics-informedpriors
Funded by NSF CDI
Cosmology
• Bayesian approaches to fitting high-level cosmological models
http://cmml2011.wikispaces.com/
Cosmic Ray Classification
• Raw image from Hubble Space Telescope: