The Role of Computer Vision
in Astronomy
Rob Fergus
New 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
• Input: image of sky
• Output:
– Absolute position
– List of objects
• Geometric hashing
(quads of stars)
– Lamdan & Wolfson
[ICCV’88]
• Widely used by
pros & amateurs
Lang, Hogg, Mierle, Blanton & Roweis,
[The Astronomical Journal, Vol. 137, 201
Removing Atmospheric Distortions
• Ground-based telescopes look through atmosphere
• Blind (online) estimation of atmospheric distortion and true
image
• Far better than “lucky” imaging (current approach)
Hirsch, Harmeling, Sra & Schölkopf , [Astronomy & Astrophysics 2011]
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 Star
s
• SVM-based models
– Smith et al. [A & A, Vol. 522, 2010]
• Generative model of galaxies
– Lang et al. [In preparation]
Data
Model
Galaxie
s
Future Directions
• Unified
Bayesian
model
• Propagate
uncertainty
from pixels
• Physicsinformed
priors
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: