Improving the accuracy of identifying objects in digital frames of one series through the procedure of preliminary identification of measurements

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.286381

Keywords:

image processing, parameter estimation, measurement identification, series of frames, catalog form

Abstract

The object of this study is images of various objects of the Solar System on a series of digital frames. The variety and quality of shooting conditions make it difficult to identify a frame with the corresponding part of the sky. This fact significantly reduces the quality indicators of detection and estimation of the position of objects of the Solar System using already known computational methods and international astronomical astrometric and photometric catalogs. To solve this problem, a procedure for preliminary identification of measurements of digital frames of one series was devised.

This procedure is based on the determination of the shift parameters between the dimensions of a frame and the forms of a catalog or another frame. Also, taking into account the possibility of forming false measurements has made it possible to increase the accuracy of identification and resistance to various kinds of destabilizing factors. Based on this, the final estimation of the shift parameters between frames was performed. Due to these features, the use of the devised preliminary identification procedure makes it possible to improve identification with reference astronomical objects and reduce the number of false detections. The study showed that when identifying frames, the fitting gives the best accuracy of binding to the starry sky. Also, the standard deviation of frame identification errors in this case is 7–10 times less than without using the devised procedure.

The procedure developed for preliminary identification of measurements of digital frames of one series was tested in practice within the framework of the CoLiTec project. It has been incorporated into the Lemur software for automated detection of new and tracking of known objects. Owing to the use of the Lemur software and the proposed procedure implemented in it, more than 700,000 measurements of various astronomical objects under study were successfully identified

Author Biographies

Sergii Khlamov, Kharkiv National University of Radio Electronics

PhD, Assistant

Department of Media Systems and Technologies

Vadym Savanevych, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Professor

Department of Systems Engineering

Vladimir Vlasenko, National Space Facilities Control and Test Center

PhD

Space Research and Communication Center

Tetiana Trunova, Kharkiv National University of Radio Electronics

Engineer, Assistant

Department of Media Systems and Technologies

Volodymyr Troianskyi , Astronomical Observatory of Odesa I. I. Mechnykov National University

PhD, Senior Researcher

Viktoriya Shvedun, National University of Civil Defence of Ukraine

Doctor of Technical Sciences, Professor, Head of Department

Scientific Department on Problems of Management in the Civil Defense Sphere

Iryna Tabakova, Kharkiv National University of Radio Electronics

PhD, Associate Professor

Department of Media Systems and Technologies

References

  1. Dearborn, D. P. S., Miller, P. L. (2014). Defending Against Asteroids and Comets. Handbook of Cosmic Hazards and Planetary Defense. Springer International Publishing 1–18. doi: https://doi.org/10.1007/978-3-319-02847-7_59-1
  2. Mykhailova, L. O., Savanevych, V. E., Sokovykova, N. S., Bezkrovnii, M. M., Khlamov, S. V., Pohorelov, A. V. (2014). Method of maximum likelihood estimation of compact group objects location on CCD-frame. Eastern-European Journal of Enterprise Technologies, 5 (4 (71)), 16–22. doi: https://doi.org/10.15587/1729-4061.2014.28028
  3. Savanevych, V. E., Khlamov, S. V., Akhmetov, V. S., Briukhovetskyi, A. B., Vlasenko, V. P., Dikov, E. N. et al. (2022). CoLiTecVS software for the automated reduction of photometric observations in CCD-frames. Astronomy and Computing, 40, 100605. doi: https://doi.org/10.1016/j.ascom.2022.100605
  4. Vavilova, I., Pakuliak, L., Babyk, I., Elyiv, A., Dobrycheva, D., Melnyk, O. (2020). Surveys, Catalogues, Databases, and Archives of Astronomical Data. Knowledge Discovery in Big Data from Astronomy and Earth Observation. Elsevier, 57–102. doi: https://doi.org/10.1016/b978-0-12-819154-5.00015-1
  5. Cavuoti, S., Brescia, M., Longo, G. (2012). Data mining and knowledge discovery resources for astronomy in the Web 2.0 age. SPIE Astronomical Telescopes and Instrumentation, Software and Cyberinfrastructure for Astronomy II, 8451, 13. doi: https://doi.org/10.1117/12.925321
  6. Chalyi, S., Levykin, I., Biziuk, A., Vovk, A., Bogatov, I. (2020). Development of the technology for changing the sequence of access to shared resources of business processes for process management support. Eastern-European Journal of Enterprise Technologies, 2 (3 (104)), 22–29. doi: https://doi.org/10.15587/1729-4061.2020.198527
  7. Khlamov, S., Savanevych, V. (2020). Big Astronomical Datasets and Discovery of New Celestial Bodies in the Solar System in Automated Mode by the CoLiTec Software. Knowledge Discovery in Big Data from Astronomy and Earth Observation. Elsevier, 331–345. doi: https://doi.org/10.1016/b978-0-12-819154-5.00030-8
  8. Troianskyi, V., Kankiewicz, P., Oszkiewicz, D. (2023). Dynamical evolution of basaltic asteroids outside the Vesta family in the inner main belt. Astronomy & Astrophysics, 672, A97. doi: https://doi.org/10.1051/0004-6361/202245678
  9. Akhmetov, V., Khlamov, S., Savanevych, V., Dikov, E. (2019). Cloud Computing Analysis of Indian ASAT Test on March 27, 2019. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&aT), 315–318. doi: https://doi.org/10.1109/picst47496.2019.9061243
  10. Oszkiewicz, D., Troianskyi, V., Galád, A., Hanuš, J., Ďurech, J., Wilawer, E. et al. (2023). Spins and shapes of basaltic asteroids and the missing mantle problem. Icarus, 397, 115520. doi: https://doi.org/10.1016/j.icarus.2023.115520
  11. Smith, G. E. (2010). Nobel Lecture: The invention and early history of the CCD. Reviews of Modern Physics, 82 (3), 2307–2312. doi: https://doi.org/10.1103/revmodphys.82.2307
  12. Savanevych, V., Khlamov, S., Briukhovetskyi, O., Trunova, T., Tabakova, I. (2023). Mathematical Methods for an Accurate Navigation of the Robotic Telescopes. Mathematics, 11 (10), 2246. doi: https://doi.org/10.3390/math11102246
  13. Kuzmyn, S. Z. (2000). Tsyfrovaia radyolokatsyia. Vvedenye v teoryiu. Kyiv: Yzdatelstvo KviTs, 428.
  14. Savanevych, V., Khlamov, S., Vlasenko, V., Deineko, Z., Briukhovetskyi, O., Tabakova, I., Trunova, T. (2022). Formation of a typical form of an object image in a series of digital frames. Eastern-European Journal of Enterprise Technologies, 6 (2 (120)), 51–59. doi: https://doi.org/10.15587/1729-4061.2022.266988
  15. Klette, R. (2014). Concise computer vision. An Introduction into Theory and Algorithms. London: Springer, 233. doi: https://doi.org/10.1007/978-1-4471-6320-6
  16. Kirichenko, L., Zinchenko, P., Radivilova, T. (2021). Classification of time realizations using machine learning recognition of recurrence plots. Advances in Intelligent Systems and Computing, 1246 AISC, 687–696. doi: https://doi.org/10.1007/978-3-030-54215-3_44
  17. Savanevych, V., Akhmetov, V., Khlamov, S., Dikov, E., Briukhovetskyi, A., Vlasenko, V. et al. (2019).Selection of the reference stars for astrometric reduction of CCD-frames. Advances in Intelligent Systems and Computing: Springer Nature Switzerland, 1080, 881–895. doi: https://doi.org/10.1007/978-3-030-33695-0_57
  18. Belov, L. A. (2021). Radioelektronika. Formirovanie stabilnykh chastot i signalov. Moscow: Izdatelstvo Iurait, 268.
  19. Lösler, M., Eschelbach, C., Riepl, S. (2018). A modified approach for automated reference point determination of SLR and VLBI telescopes: First investigations at Satellite Observing System Wettzell. Technisches Messen, 85, 616–626. doi: https://doi.org/10.1515/teme-2018-0053
  20. Akhmetov, V., Khlamov, S., Tabakova, I., Hernandez, W., Nieto Hipolito, J. I., Fedorov, P. (2019). New approach for pixelization of big astronomical data for machine vision purpose. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 1706–1710. doi: https://doi.org/10.1109/isie.2019.8781270
  21. Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., Terzopoulos, D. (2021). Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (7), 3523–3542. doi: https://doi.org/10.1109/tpami.2021.3059968
  22. Dadkhah, M., Lyashenko, V. V., Deineko, Z. V., Shamshirband, S., Jazi, M. D. (2019). Methodology of wavelet analysis in research of dynamics of phishing attacks. International Journal of Advanced Intelligence Paradigms, 12(3/4), 220–238. doi: https://doi.org/10.1504/ijaip.2019.098561
  23. Kirichenko, L., Saif, A., Radivilova, T. (2020). Generalized Approach to Analysis of Multifractal Properties from Short Time Series. International Journal of Advanced Computer Science and Applications, 11 (5), 183–198. doi: https://doi.org/10.14569/ijacsa.2020.0110527
  24. Hampson, K. M., Gooding, D., Cole, R., Booth, M. J. (2019). High precision automated alignment procedure for two-mirror telescopes. Applied Optics, 58 (27), 7388–7391. doi: https://doi.org/10.1364/ao.58.007388
  25. Parimucha, Š., Savanevych, V. E., Briukhovetskyi, O. B., Khlamov, S. V., Pohorelov, A. V., Vlasenko, V. P. (2019). CoLiTecVS – A new tool for an automated reduction of photometric observations. Contributions of the Astronomical Observatory Skalnate Pleso, 49 (2), 151–153.
  26. Khlamov, S., Vlasenko, V., Savanevych, V., Briukhovetskyi, O., Trunova, T., Chelombitko, V., Tabakova, I. (2022). Development of computational method for matched filtration with analytical profile of the blurred digital image. Eastern-European Journal of Enterprise Technologies, 5 (4 (119)), 24–32. doi: https://doi.org/10.15587/1729-4061.2022.265309
  27. Burger, W., Burge, M. (2010). Principles of digital image processing: core algorithms. Springer Science & Business Media, 332. doi: https://doi.org/10.1007/978-1-84800-195-4
  28. Khlamov, S., Tabakova, I., Trunova, T. (2022). Recognition of the astronomical images using the Sobel filter. 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). doi: https://doi.org/10.1109/iwssip55020.2022.9854425
  29. Kashuba, S., Tsvetkov, M., Bazyey, N., Isaeva, E., Golovnia, V. (2018). The Simeiz plate collection of the ODESSA astronomical observatory. 11th Bulgarian-Serbian Astronomical Conference, 207–216.
  30. Li, T., DePoy, D., Marshall, J., Nagasawa, D., Carona, D., Boada, S. (2014). Monitoring the atmospheric throughput at Cerro Tololo Inter-American Observatory with aTmCam. Ground-based and Airborne Instrumentation for Astronomy V, 9147, 2194–2205. doi: https://doi.org/10.1117/12.2055167
  31. Zacharias, N., Finch, C. T., Girard, T. M., Henden, A., Bartlett, J. L., Monet, D. G., Zacharias, M. I. (2013). The fourth us naval observatory CCD astrograph catalog (UCAC4). The Astronomical Journal, 145 (2), 44. doi: https://doi.org/10.1088/0004-6256/145/2/44
  32. Luo, X., Gu, S., Xiang, Y., Wang, X., Yeung, B., Ng, E. et al. (2022). Active longitudes and starspot evolution of the young rapidly rotating star USNO-B1.0 1388−0463685 discovered in the Yunnan–Hong Kong survey. Monthly Notices of the Royal Astronomical Society, 514 (1), 1511–1521. doi: https://doi.org/10.1093/mnras/stac1406
  33. CoLiTec project. Available at: https://www.colitec.space
  34. Khlamov, S., Savanevych, V., Briukhovetskyi, O., Tabakova, I., Trunova, T. (2022). Data Mining of the Astronomical Images by the CoLiTec Software. CEUR Workshop Proceedings, 3171, 1043–1055.
  35. Kobzar, A. I. (2006), Prikladnaia matematicheskaia statistika. Dlia inzhenerov i nauchnykh rabotnikov. Moscow: FIZMATLI, 816.
  36. Sergienko, A. B. (2011). Tcifrovaia obrabotka signalov. Saint Petersburg: BKhV-SPb, 768.
  37. Shvedun, V. O., Khlamov, S. V. (2016). Statistical modeling for determination of perspective number of advertising legislation violations. Actual Problems of Economics, 184 (10), 389–396.
  38. Zhang, Y., Zhao, Y., Cui, C. (2002). Data mining and knowledge discovery in database of astronomy. Progress in Astronomy, 20 (4), 312–323.
  39. Steger, C., Ulrich, M., Wiedemann, C. (2018). Machine vision algorithms and applications. John Wiley & Sons, 516.
  40. Buslov, P., Shvedun, V., Streltsov, V. (2018). Modern Tendencies of Data Protection in the Corporate Systems of Information Consolidation. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), 285–288. doi: https://doi.org/10.1109/infocommst.2018.8632089
  41. Рetrychenko, A., Levykin, I., Iuriev, I. (2021). Improving a method for selecting information technology services. Eastern-European Journal of Enterprise Technologies, 2 (2 (110)), 32–43. doi: https://doi.org/10.15587/1729-4061.2021.229983
  42. Qiang, Z., Bai, X., Zhang, Q., Lin, H. (2019). A CME Automatic Detection Method Based on Adaptive Background Learning Technology. Advances in Astronomy, 2019, 1–14. doi: https://doi.org/10.1155/2019/6582104
  43. Baranova, V., Zeleniy, O., Deineko, Z., Bielcheva, G., Lyashenko, V. (2019). Wavelet Coherence as a Tool for Studying of Economic Dynamics in Infocommunication Systems. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), 336–340. doi: https://doi.org/10.1109/picst47496.2019.9061301
  44. Sen, S., Agarwal, S., Chakraborty, P., Singh, K. (2022). Astronomical big data processing using machine learning: A comprehensive review. Experimental Astronomy, 53 (1), 1–43. doi: doi: https://doi.org/10.48550/arXiv.1904.07248
  45. Feigelson, E. D., Babu, G. J., Caceres, G. A. (2018). Autoregressive Times Series Methods for Time Domain Astronomy. Frontiers in Physics, 6, 80. doi: https://doi.org/10.3389/fphy.2018.00080
  46. Bogod, V. M., Svidskiy, P. M., Kurochkin, E. A., Shendrik, A. V., Everstov, N. P. (2018). A Method of Forecasting Solar Activity Based on Radio Astronomical Observations. Astrophysical Bulletin, 73 (4), 478–486. doi: https://doi.org/10.1134/s1990341318040119
Improving the accuracy of identifying objects in digital frames of one series through the procedure of preliminary identification of measurements

Downloads

Published

2023-08-31

How to Cite

Khlamov, S., Savanevych, V., Vlasenko, V., Trunova, T., Troianskyi , V., Shvedun, V., & Tabakova, I. (2023). Improving the accuracy of identifying objects in digital frames of one series through the procedure of preliminary identification of measurements . Eastern-European Journal of Enterprise Technologies, 4(2 (124), 35–43. https://doi.org/10.15587/1729-4061.2023.286381