LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE
Leonid Timchenko
tumchenko_li@gsuite.duit.edu.uaState University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0001-5056-5913
Natalia Kokriatskaia
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0003-0090-3886
Volodymyr Tverdomed
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0002-0695-1304
Natalia Kalashnik
National Pirogov Memorial Medical University, Vinnytsia, Ukraine (Ukraine)
http://orcid.org/0000-0001-5312-3280
Iryna Shvarts
Vinnytsia National Technical University, Vinnytsia, Ukraine (Ukraine)
http://orcid.org/0000-0003-4344-5213
Vladyslav Plisenko
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0002-5970-2408
Dmytro Zhuk
State University of Infrastructure and Technology, Kyiv, Ukraine (Ukraine)
http://orcid.org/0000-0001-8951-5542
Saule Kumargazhanova
D.Serikbayev East Kazakhstan State Technical University, Ust-Kamenogorsk, Kazakhstan (Kazakhstan)
http://orcid.org/0000-0002-6744-4023
Abstract
The article was aimed at studying the process of learning by the local difference threshold when filtering normal white noise. The existing learning algorithms for image processing were analyzed and their advantages and disadvantages were identified. The influence of normal white noise on the recognition process is considered. A method for organizing the learning process of the correlator with image preprocessing by the GQP method has been developed. The dependence of the average value of readings of the rank CCF (RCCF) of GQPs of the reference and current images, representing realizations of normal white noise, on the probability of formation of readings of zero GQP is determined. Two versions of the learning algorithm according to the described learning method are proposed. A technique for determining the algorithm efficiency estimate is proposed.
Keywords:
training, local difference threshold, filtering normal white noiseReferences
Bochkarev A. M.: Correlation-Navigation Navigation Systems. Foreign radio electronics 9, 1981, 12–16.
Google Scholar
Dougherty E. R.: Digital Image Processing Methods. CRC Press, Boca Raton 2020) [http://doi.org/10.1201/9781003067054].
DOI: https://doi.org/10.1201/9781003067054
Google Scholar
Gan Woon Siong: Signal Processing and Image Processing for Acoustical Imaging. Springer Singapore, 2020 [http://doi.org/10.1007/978-981-10-5550-8].
DOI: https://doi.org/10.1007/978-981-10-5550-8
Google Scholar
Kondratiuk S., Kruchynin K., Krak I., Kruchinin S.: Information technology for security system based on cross platform software, NATO Science for Peace and Security Series A: Chemistry and Biology, 2018, 331–339.
DOI: https://doi.org/10.1007/978-94-024-1304-5_25
Google Scholar
Kondratiuk S., Krak I.: Dactyl Alphabet Modeling and Recognition Using Cross Platform Software. Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, 8478417, 2018, 420–423.
DOI: https://doi.org/10.1109/DSMP.2018.8478417
Google Scholar
Kozlovska T., Pavlov S.: Optoelectronic Means of Diagnosing Human Pathologies Associated with Peripheral Blood Circulation. Academic Publishing, Beau Bassin 71504, Mauritius 2019.
Google Scholar
Krak I. V., Kryvonos I. G., Kulias A. I.: Applied aspects of the synthesis and analysis of voice information. Cybernetics and Systems Analysis 49(4), 2013, 89–596.
DOI: https://doi.org/10.1007/s10559-013-9545-9
Google Scholar
Kutaev Y. F.: Systemic correlation-extreme measurement of coordinates with generalized Q-preparation of images: Ph.D. thessis. Vinnitsa, 1989.
Google Scholar
Pogrebnoy V. A.: Airborne signal processing systems. Scientific thought, Kiev 1984.
Google Scholar
Pratt W.: Digital image processing. In 2 books. John Wiley & Sons, Inc., 1982.
Google Scholar
Sacerdoti F. M.: Digital Image Processing. In: Sacerdoti, F., Giordano, A., Cavaliere, C. (eds): Advanced Imaging Techniques in Clinical Pathology. Current Clinical Pathology. Humana Press, New York 2016 [http://doi.org/10.1007/978-1-4939-3469-0_2].
DOI: https://doi.org/10.1007/978-1-4939-3469-0
Google Scholar
Timchenko L. I., Kokriatskaia N. I., Nakonechna S., Poplavskaia A. A., Stepaniuk D. S., Gromaszek K. and Rakhmetullina S.: Analysis of computational processes of pyramidal and parallel-hierarchical processing of information, Proc. SPIE 10808, 2018, 1080822.
Google Scholar
Timchenko L. I., Kutaev Y. F., Chepornyuk S. V., Grudin M. A., Harvey D. M., Gertsiy A. A.: A Brain Like Approach to Multistage Hierarchial Image, Lecture Notes in Computer Sciense. Image Analysis and Processing 1311, 1997, 246–253.
DOI: https://doi.org/10.1007/3-540-63508-4_129
Google Scholar
Trishch R., Nechuiviter O., Vasilevskyi O., Dyadyura K., Tsykhanovska I., Yakovlev M.: Qualimetric method of assessing risks of low quality products, MM Science Journal 4, 2021, 4769–4774.
DOI: https://doi.org/10.17973/MMSJ.2021_10_2021030
Google Scholar
Tulbure A., Tulbure A.: The use of image recognition systems in manufacturing processes. IEEE International Conference on Automation, Quality and Testing, Robotics 2018.
Google Scholar
Wójcik W., Pavlov S., Kalimoldayev M.: Information Technology in Medical Diagnostics II. Taylor & Francis Group, CRC Press, Balkema book, London 2019.
DOI: https://doi.org/10.1201/9780429057618
Google Scholar
Authors
Leonid Timchenkotumchenko_li@gsuite.duit.edu.ua
State University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0001-5056-5913
Authors
Natalia KokriatskaiaState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0003-0090-3886
Authors
Volodymyr TverdomedState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0002-0695-1304
Authors
Natalia KalashnikNational Pirogov Memorial Medical University, Vinnytsia, Ukraine Ukraine
http://orcid.org/0000-0001-5312-3280
Authors
Iryna ShvartsVinnytsia National Technical University, Vinnytsia, Ukraine Ukraine
http://orcid.org/0000-0003-4344-5213
Authors
Vladyslav PlisenkoState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0002-5970-2408
Authors
Dmytro ZhukState University of Infrastructure and Technology, Kyiv, Ukraine Ukraine
http://orcid.org/0000-0001-8951-5542
Authors
Saule KumargazhanovaD.Serikbayev East Kazakhstan State Technical University, Ust-Kamenogorsk, Kazakhstan Kazakhstan
http://orcid.org/0000-0002-6744-4023
Statistics
Abstract views: 213PDF downloads: 184
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Leonid Timchenko, Natalia Kokriatska, Volodymyr Tverdomed, Iryna Yepifanova, Yurii Didenko, Dmytro Zhuk, Maksym Kozyr, Iryna Shakhina, ARCHITECTURAL AND STRUCTURAL AND FUNCTIONAL FEATURES OF THE ORGANIZATION OF PARALLEL-HIERARCHICAL MEMORY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 1 (2024)
- Leonid Timchenko, Natalia Kokriatskaia, Volodymyr Tverdomed, Anatolii Horban, Oleksandr Sobovyi, Liudmyla Pogrebniak, Nelia Burlaka, Yurii Didenko, Maksym Kozyr, Ainur Kozbakova, NEUROBIOLOGICAL PROPERTIES OF THE STRUCTURE OF THE PARALLEL-HIERARCHICAL NETWORK AND ITS USAGE FOR PATTERN RECOGNITION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 3 (2024)
