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Computational Visual Media  2020, Vol. 6 Issue (4): 445-454    doi: 10.1007/s41095-020-0198-0
Research Article     
Estimating camera parameters from starry night photographs
Naoto Ishikawa1,(✉)(),Yoshinori Dobashi1()
1 Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
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Abstract  

We propose an efficient, specific method for estimating camera parameters from a single starry night image. Such an image consists of a collection of disks representing stars, so traditional estimation methods for common pictures do not work. Our method uses a database, a star catalog, that stores the positions of stars on the celestial sphere. Our method computes magnitudes (i.e., brightnesses) of stars in the input image and uses them to find the corresponding stars in the star catalog. Camera parameters can then be estimated by a simple geometric calculation. Our method is over ten times faster and more accurate than a previous method.



Key wordsastrophotography      constellation      star identification      pattern matching     
Received: 15 July 2020      Published: 30 November 2020
Corresponding Authors: Naoto Ishikawa     E-mail: ishikawa@ime.ist.hokudai.ac.jp;doba@ ime.ist.hokudai.ac.jp
About author: Naoto Ishikawa is a master’s course student at Hokkaido University in the Graduate School of Information Science and Technology. He is interested in image processing.|Yoshinori Dobashi has been anassociate professor at Hokkaido Universityin the Graduate School of Information Science and Technology, Japan since 2006. His research interests center on computer graphics, including realistic image synthesis, efficient rendering, and sound modeling for virtual reality applications. Dobashi received his B.E., M.E. and Ph.D. degrees in engineering in 1992, 1994, and 1997, respectively, from Hiroshima University. He worked at Hiroshima City University from 1997 to 2000 as a research associate.
Cite this article:

Naoto Ishikawa,Yoshinori Dobashi. Estimating camera parameters from starry night photographs. Computational Visual Media, 2020, 6(4): 445-454.

URL:

http://cvm.tsinghuajournals.com/10.1007/s41095-020-0198-0     OR     http://cvm.tsinghuajournals.com/Y2020/V6/I4/445

Fig. 1 Star detection process.
Fig. 2 Estimation of the camera parameters.
Fig. 3 Reliable triplets selection.
triplet
trialnmijk
131123
241134
351145
451234
561156
661245
732124
842135
Table 1 Example of triplets selection
parametertolerance
right ascension2
declination2
camera orientation5
view angle3
Table 2 Tolerances for accuracy
Fig. 4 Example synthetic images.
Astrometry.netOur method
average time13.55 s1.10 s
accuracy70.20%100.00%
Table 3 Comparison using synthetic images
Fig. 5 Example real images.
Astrometry.netOur method
average time32.59 s2.86 s
accuracy72.97%89.19%
Table 4 Comparison using real images
Fig. 6 Comparison of input and rendered images.
Fig. 7 Accuracy and computation time for different numbers of stars.
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