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Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/19281

Title: Astrometry.net: Automatic Recognition and Calibration of Astronomical Images
Authors: Lang, Dustin
Advisor: Roweis, Sam
Hogg, David W.
Department: Computer Science
Keywords: Astrometry
Computer vision
Astronomy
Issue Date: 3-Mar-2010
Abstract: We present Astrometry.net, a system for automatically recognizing and astrometrically calibrating astronomical images, using the information in the image pixels alone. The system is based on the geometric hashing approach in computer vision: We use the geometric relationships between low-level features (stars and galaxies), which are relatively indistinctive, to create geometric features that are distinctive enough that we can recognize images that cover less than one-millionth of the area of the sky. The geometric features are used to generate rapidly hypotheses about the location---the pointing, scale, and rotation---of an image on the sky. Each hypothesis is then evaluated in a Bayesian decision theory framework in order to ensure that most correct hypotheses are accepted while false hypotheses are almost never accepted. The feature-matching process is accelerated by using a new fast and space-efficient kd-tree implementation. The Astrometry.net system is available via a web interface, and the software is released under an open-source license. It is being used by hundreds of individual astronomers and several large-scale projects, so we have at least partially achieved our goal of helping ``to organize, annotate and make searchable all the world's astronomical information.''
URI: http://hdl.handle.net/1807/19281
Appears in Collections:Doctoral
Department of Computer Science - Doctoral theses

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