Towards Science in Chile with LSST Viña del Mar, December 2016 AstroCV: A Computer Vision Library for Astronomy Alejandro Sazo (UTFSM, Engineer) Roberto González (PUC, Astronomer) Felipe San Martín (Universidad de Chile, Engineer) Roberto Muñoz Instituto de Astrofísica Pontificia Universidad Católica de Chile
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Towards Science in Chile with LSST Viña del Mar, December 2016
AstroCV: A Computer Vision Library for Astronomy
Alejandro Sazo (UTFSM, Engineer) Roberto González (PUC, Astronomer) Felipe San Martín (Universidad de Chile, Engineer)
Roberto Muñoz Instituto de Astrofísica
Pontificia Universidad Católica de Chile
Surveys before CCDsBefore CCDs became the standard in Astronomy, the major surveys were conducted using photographic plates.
Hubble galaxy classification and Sersic profiles were derived at that epoch.
Roberto Muñoz
Advent of large and rich surveys
Roberto Muñoz
Time span: 15 yearsArea: 14,555 deg2
# sources: 469,053,874
Time span: 10 yearsArea: 25,000 deg2
# sources: 37,000,000,000
CCDs became more sensitive and mosaics were affordable
Photometric Spectroscopic UV-IR coverage High cadence
Galaxies in the UniverseGalaxies come in many flavors
Galaxy Zoo and KaggleGalaxyZoo: One of the most successful citizen science project in Astronomy. Started in 2007.
Visual classification of about 900.000 galaxies
Roberto Muñoz
SDSS Hubble ACS and WFC3
Kaggle competition37 classes from the GalaxyZoo decision tree
Why Computer Vision?
Interdisciplinary field: CS, Physics, Math, AI
Computers gain high-level understanding
Roberto Muñoz
Ongoing (DES, VVV) and coming surveys (LSST) will produce enormous datasets.
Compute features for later classification
Automatic detection, segmentation and classification
Features and classificationAstronomers have been computing features for years: luminosity, color, µr, reff, Sersic index, etc
Major improvements for feature descriptor: SIFT, HOG, GISTand classification: SVM, Random forest, Neural network
Roberto Muñoz
Missrate and FPS
Roberto Muñoz
2003
2014
FPS
Region-based FullyConvolutional Networks = 6 FPS
Dai et al. 2016Benenson et al. 2014
Dollár et al. 2014
AstroCV libraryWe are developing a computer vision library optimised for astronomical datasets. Our goal is doing on-the-fly data processing using GPUs.
The library is being developed in C++ and CUDA. The feature descriptor is based on HOG and the classification in Random forest.
We process 30,000 x 30,000 pixel image in 25 seconds, single GPU. Fast Deep Learning will be 8 minutes.