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An efficient algorithm for determining positions of astronomical objects in the Deep Sky Object pictures

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents an algorithm for digital image processing of astronomical objects in order to effectively determine the position of these objects. The proposed method has been optimized due to its effectiveness of removing noise and distortion caused by atmospheric turbulence and imperfections in long exposure photography of astronomical objects. This solution is ready for implementation in a system for automatic identification of stars in the recorded images. Such a system is designed for GoTo circuits at telescope’s drives, which can automatically point a telescope to astronomical objects. The method was verified by simulation in MATLAB program on real images of astronomical objects.
Rocznik
Strony
679--684
Opis fizyczny
Bibliogr. 26 poz., rys., wykr., tab.
Twórcy
autor
  • Faculty of Electronics and Information Science, Koszalin University of Technology, 2. Sniadeckich St., 75-453 Koszalin, Poland
autor
  • Faculty of Electronics and Information Science, Koszalin University of Technology, 2. Sniadeckich St., 75-453 Koszalin, Poland
  • Faculty of Electronics and Information Science, Koszalin University of Technology, 2. Sniadeckich St., 75-453 Koszalin, Poland
Bibliografia
  • [1] G.D. Roth, Handbook of Practical Astronomy, Springer-Verlag, Berlin, 2009.
  • [2] H.G. Ziegler, Telescope Mountings, Drives, and Electrical Equipment, Compendium of Practical Astronomy, vol. 1, chap. 5, Springer, Berlin, 1994.
  • [3] R. Suszynski, “A stand-alone station and DSP method for deep sky objects astrophotography”, Int. J. Electronics and Telecommunications 60 (2), 157–164 (2014).
  • [4] R. Suszynski, “Digital processing of CCD images for auto-guiding astrophotography system”, Proc. 15th Int. Conf. on Mixed Design of Integrated Circuits and Systems (MIXDES) 1, 559–562 (2008).
  • [5] R. Suszynski, K. Wawryn, and R. Wirski, “2D signal processing for identification and tracking moving object”, Przegląd Elektrotechniczny 87 (10), 126–129 (2011).
  • [6] R. Suszynski, “Convolution method for CCD images processing for DSO astrophotography”, Proc. IEEE 52nd Int. Midwest Symp. Circuits and Systems (MWSCAS) 1 (4), 762–765 (2009).
  • [7] R. Suszynski and K. Wawryn, “An improvement of stars’ centroid determination using PSF-fitting method”, Proc. IEEE Int. Conf. Signals and Electronics Systems ICSES 1, 4 (2014).
  • [8] P.C. McGuire, D.G. Sandler, M.L. Hart, and T.A. Rhoadarmer, “Adaptive optics: neural networks wavefront sensing, reconstruction, and prediction”, Proc. 194th W.E. Heracus Seminar 1, CD-ROM (1998).
  • [9] S. Thomas, T. Fusco, A. Tokovinin, M. Nicolle, V. Michau, and G. Rousset, “Comparison of centroid computation algorithms in a Shack-Hartmann sensor”, Monthly Notices of the Royal Astronomical Society 371, 323–336 (2006).
  • [10] K.L. Baker and M.M. Moalem, “Iteratively weighted centroid of Shack-Hartmann wave-front sensors”, Opt. Express 15, 5147–5159 (2007).
  • [11] L.A. Poyneer, D.W. Palmer, K.N. LaFortune, and B. Bauman, “Experimental results for correlation-based wave-front sensing”, SPIE Advanced Wavefront Control 5894, 58940N (2005).
  • [12] A. Vyas, M.B. Roopashree, and B.R. Prasad, “Cetroid detection by Gaussian pattern matching in adaptive optics”, Int. J. Computer Applications 1, 30–36 (2010).
  • [13] R.J. Noll, “Zernike polynomials and atmosphere turbulences”, JOSA 66, 207–211 (1976).
  • [14] A. Berghi, A. Canedese, and A. Masiero, “Atmospheric turbulence prediction: a pca approach”, Proc. IEEE 46th Conf. Decision and Control 1, 572–577 (2007).
  • [15] B.D. Jeffs and J.C. Christou, “Blind bayesian restoration of adaptive optics telescope images using generalized gaussian markov random field models”, Proc. Conf. on Adaptive Optics and Telescope Systems SPIE 3353, CD-ROM (1998).
  • [16] R. Suszynski, “Stand-alone station for deep space objects astrophotography”, Proc. IEEE 52nd Int. Midwest Symposium on Circuits and Systems (MWSCAS) 1 (4), 333–336 (2009).
  • [17] W. Zhang, Z. Jiang, H. Zhang, and J. Luo, “Optical image simulation system for space surveillance”, Proc. IEEE 26th Int. Parallel and Distributed Processing Symp. 1, CD-ROM (2012).
  • [18] C. Li, Y. Zhang, C. Zheng, and X. Hu, “Implementing high-performance intensity model with blur effect on gpus for large-scale star image simulation”, Proc. Int. Conf. on Image and Graphics 1, CD-ROM (2013).
  • [19] R. Szeliski, Computer Vision, Algorithms and Applications, Springer-Verlag, London, 2011.
  • [20] R. Suszynski, K. Wawryn, and R. Wirski, “2D image processing for auto-guiding system”, Proc. IEEE 54th Int. Midwest Symp. on Circuits and Systems MWSCAS 1, CD-ROM (2011).
  • [21] K. Wawryn, R. Wirski, and B. Strzeszewski, “Implementation of finite impulse response systems using rotation structures”, Proc. Int. Symp. Information Theory and Its Applications ISITA2010 1, CD-ROM (2010).
  • [22] R.P. Roesser, “A discrete state-space model for linear image processing”, IEEE Trans. Automat. Contr. 20, 1–10 (1975).
  • [23] D.E. Dudgeon and R.M. Mersereau, Multidimensional Signal Processing, Prentice-Hall, Englewood Cliffs, 1984.
  • [24] Ch.-S. Li and S-Z. Jin, “The implement of high speed correlation tracking algorithm based on FPGA in space solar telescope”, Proc. 8th Int. Conf. on Signal Processing 1, CD-ROM (2006).
  • [25] K. Wawryn and R. Suszynski, “Low power 9-bit pipelined A/D and 8-bit self-calibrated D/A converters for a DSP system”, Bull. Pol. Ac.: Tech. 61 (4), 979–688 (2013).
  • [26] R. Suszynski and K. Wawryn, “Rapid prototyping of algorithmic A/D converters based on FPAA devices”, Bull. Pol. Ac.: Tech. 61 (3), 691–696 (2013).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9d697b59-99e7-4398-98b2-2f5d635a4123
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