Towards building a Crowd-Sourced Sky Map

Dustin Lang, David Hogg, Bernhard Schölkopf
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:549-557, 2014.

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 pixel-rank 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-lang14, title = {{Towards building a Crowd-Sourced Sky Map}}, author = {Lang, Dustin and Hogg, David and Schölkopf, Bernhard}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {549--557}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/lang14.pdf}, url = {https://proceedings.mlr.press/v33/lang14.html}, 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 pixel-rank 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.} }
Endnote
%0 Conference Paper %T Towards building a Crowd-Sourced Sky Map %A Dustin Lang %A David Hogg %A Bernhard Schölkopf %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-lang14 %I PMLR %P 549--557 %U https://proceedings.mlr.press/v33/lang14.html %V 33 %X 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 pixel-rank 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.
RIS
TY - CPAPER TI - Towards building a Crowd-Sourced Sky Map AU - Dustin Lang AU - David Hogg AU - Bernhard Schölkopf BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-lang14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 549 EP - 557 L1 - http://proceedings.mlr.press/v33/lang14.pdf UR - https://proceedings.mlr.press/v33/lang14.html AB - 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 pixel-rank 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. ER -
APA
Lang, D., Hogg, D. & Schölkopf, B.. (2014). Towards building a Crowd-Sourced Sky Map. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:549-557 Available from https://proceedings.mlr.press/v33/lang14.html.

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