Towards building a Crowd-Sourced Sky Map

Dustin Lang, David W. Hogg, Bernhard Schölkopf

    Research output: Contribution to journalConference article

    Abstract

    We describe a system that builds a high dynamic-range and wide-angle image of the night sky by combining a large set of input images. The method makes use of pixelrank information in the individual input images to improve a "consensus" pixel rank in the combined image. Because it only makes use of ranks and the complexity of the algorithm is linear in the number of images, the method is useful for large sets of uncalibrated images that might have undergone unknown non-linear tone mapping transformations for visualization or aesthetic reasons. We apply the method to images of the night sky (of unknown provenance) discovered on the Web. The method permits discovery of astronomical objects or features that are not visible in any of the input images taken individually. More importantly, however, it permits scientific exploitation of a huge source of astronomical images that would not be available to astronomical research without our automatic system.

    Original languageEnglish (US)
    Pages (from-to)549-557
    Number of pages9
    JournalJournal of Machine Learning Research
    Volume33
    StatePublished - Jan 1 2014
    Event17th International Conference on Artificial Intelligence and Statistics, AISTATS 2014 - Reykjavik, Iceland
    Duration: Apr 22 2014Apr 25 2014

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Statistics and Probability
    • Artificial Intelligence

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  • Cite this

    Lang, D., Hogg, D. W., & Schölkopf, B. (2014). Towards building a Crowd-Sourced Sky Map. Journal of Machine Learning Research, 33, 549-557.