LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE

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 noise

References

Bochkarev A. M.: Correlation-Navigation Navigation Systems. Foreign radio electronics 9, 1981, 12–16.

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

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

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

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

Kozlovska T., Pavlov S.: Optoelectronic Means of Diagnosing Human Pathologies Associated with Peripheral Blood Circulation. Academic Publishing, Beau Bassin 71504, Mauritius 2019.

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

Kutaev Y. F.: Systemic correlation-extreme measurement of coordinates with generalized Q-preparation of images: Ph.D. thessis. Vinnitsa, 1989.

Pogrebnoy V. A.: Airborne signal processing systems. Scientific thought, Kiev 1984.

Pratt W.: Digital image processing. In 2 books. John Wiley & Sons, Inc., 1982.

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

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.

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

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

Tulbure A., Tulbure A.: The use of image recognition systems in manufacturing processes. IEEE International Conference on Automation, Quality and Testing, Robotics 2018.

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

Timchenko, L., Kokriatskaia, N., Tverdomed, V., Kalashnik, N., Shvarts, I., Plisenko, V., … Kumargazhanova, S. (2023). LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(2), 69–73. https://doi.org/10.35784/iapgos.3664

LOCAL DIFFERENCE THRESHOLD LEARNING IN FILTERING NORMAL WHITE NOISE

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