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Abstrakty
This paper explores the application of convolutional neural networks in the field of amateur astronomy. The authors have employed the available astronomical datasets to develop a detector for identifying astronomical objects from the Messier catalog. A concept framework for creating such a detector for astronomical objects using artificial intelligence tools in the form of a detector based on convolutional neural networks is presented. Augmentation and pre-processing procedures have been used to extend the feature distribution in the training set. Examples confirming the effectiveness of the proposed detector of astronomical objects are presented.
Rocznik
Tom
Strony
461--479
Opis fizyczny
Bibliogr. 39 poz., il., tab., wykr.
Twórcy
autor
- Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice, Poland
autor
- Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice, Poland
Bibliografia
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- 33. E. Spyrou, H. Le Borgne, T. Mailis, E. Cooke, Y. Avrithis, N. O’Connor, Fusing MPEG-7 visual descriptors for image classification, [in:] ICANN 2005 – International Conference on Artificial Neural Networks, 11–15 September, Warsaw, Poland, 2005, doi: 10.1007/11550907_134.
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- 38. Github: The TensorFlow Object Detection API, https://github.com/tensorflow/models/tree/master/research/object_detection, accessed February 17, 2021.
- 39. The STScI Digitized Sky Survey, Space Telescope Science Institute in Baltimore, Maryland, https://archive.stsci.edu/cgi-bin/dss_form?target=M8&resolver=SIMBAD, accessed February 15, 2021.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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