Abstract
On the base of the first-order disproportion functions, an algorithm recognizing fragments of standard images under conditions when the analyzed signal contains these fragments in a distorted form due to passing through a nonlinear device, the static characteristic of which can be represented by a polynomial with unknown coefficients, is developed. Both, continuous signals and those, described by discrete pixel brightness values of a video image, are considered with the presence of additive impulse noises.
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References
Barhatov, V.A.: Obnaruzhenie signalov i ih klassifikacija s pomow’ju raspoznavanija obrazov. Defectoscopiya 4, 14–27 (2006)
Deglina, J.U.B.: Nejrosetevoj algoritm raspoznavanija signalov akusticheskoj jemissii. SHtuchnij іntelekt 4, 731–734 (2006)
Spravochnaja sistema po moduljam Digispot II. http//redmine.digispot.ru/…/digispot.ru/projects/digispot/wiki/WikiStart
Venediktov, M.D., Markov, V.V., Eydus, G.S.: Asinhronnyie adresnyie sistemyi svyazi. Svyaz, M. (1968)
Voishvillo, G.V.: Amplification devices Textbook for High School’s.Radio and Communications, M. (1983)
Putjatin, E.P.: Normalizacija i raspoznavanie izobrazhenij. http://sumschool.sumdu.edu.ua/is-02/rus/lectures/pytyatin/pytyatin.htm
Bezruk, V.M., Ivanenko, S.A.: Selection and recognition of the specified radio signals in the SW band. J. Inf. Telecommun. Sci. 9(2), 21–25 (2018)
Shevchuk, B.M., Zadiraka, V.K., Fraier, S.V., Luts, V.K.: Operatyvne rozpiznavannia frahmentiv i kompleksiv syhnaliv ta vydilennia obiektiv videodanykh zasobamy obiektnykh system bezprovidnykh merezh. J. Iskusstvennyiy intellekt 3, 275–283 (2013)
Muryigin, K.V.: Obnaruzhenie avtomobilnyih nomernyih znakov s ispolzovaniem predvaritelnoy obrabotki kandidatov. J. Iskusstvennyiy intellekt 3, 193–199 (2013)
Artificial Neural Networks: Concepts and Theory. IEEE Computer Society Press (1992)
Osovskij, S.: Nejronnye seti dlja obrabotki informacii. Finansy i statistika, M. (2004)
Ayinde, B.O., Inanc, T., Zurada, J.M.: Regularizing deep neural networks by enhancing diversity in feature extraction. IEEE Trans. Neural Netw. Learn. Syst. 30, 2650–2661 (2019)
Lazorenko, O.V., Lazorenko, S.V., CHernogor, L.F.: Primenenie vejvlet-analiza k zadache obnaruzhenija sverhshirokopolosnyh signalov na fone pomeh. Radiofizika i radioastronomija 1(7), 46–63 (2002)
Avramenko, V.V., Slepushko, N.JU.: Raspoznavanie jetalonnyh signalov pri nepolnoj informacii o harakteristikah pomeh. Vіsnik SumDu 4, 13–18 (2009)
Avramenko, V.V.: Harakteristiki neproporcional’nostey i ih primeneniya pri reshenii zadach diagnostiki. Vestnik SumGU 16, 12–20 (2000)
Kalashnikov, V., Avramenko, V.V., Demianenko, V.N., Kalashnykova, N.: Fragment-aided recognition of images under poor lighting and additive impulse noises. Procedia Comput. Sci. 162, 487–495 (2019)
Karpenko, A.P.: Integral’nye harakteristiki neproporcional’nosti chislovyh funkcij i ih primenenie v diagnostike. Vestnik SumGU 16, 20–25 (2000)
Kalashnikov, V.V., Avramenko, V.V., Kalashnykova, N.I.: Derivative disproportion functions for pattern recognition. In: Watada, J., Tan, S.C., Vasant, P., Padmanabhan, E., Jain, L.C. (eds.) Unconventional Modelling, Simulation, and Optimization of Geoscience and Petroleum Engineering, pp. 95–104. Springer, Heidelberg (2018)
Kalashnikov, V.V., Avramenko, V.V., Slipushko, N.Y., Kalashnykova, N.I., Konoplyanchenko, A.E.: Identification of quasi-stationary dynamic objects with the use of derivative disproportion functions. Procedia Comput. Sci. 108(C), 2100–2109 (2017)
Kalashnikov, V.V., Avramenko, V.V., Kalashnykova, N.I., Kalashnikov Jr., V.V.: A cryptosystem based upon sums of key functions. Int. J. Comb. Optim. Probl. Inform. 8, 31–38 (2017)
Kalashnykova, N., Avramenko, V.V., Kalashnikov, V.: Sums of key functions generating cryptosystems. In: Rodrigues, J.M.F., Cardoso, P.J.S., Monteiro, J., Lam, R., Krzhizhanovskaya, V.V., Lees, M.H., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2019. LNCS, vol. 11540, pp. 293–302. Springer, Cham (2019)
Jack, K.: Video Demystified. A Handbook for the Digital Engineer (5th Edition) (2007)
Tereschuk, R.M., Tereschuk, K.M., Sedov, S.A.: Poluprovodnikovyie priemno_usilitelnyie ustroystva, spravochnik radiolyubitelya, Kiev, «Naukova dumka» (1989)
Luzin, V.I., Nikitin, N.P.: Osnovyi televideniya. Uchebnoe elektronnoe tekstovoe, izdanie GOU VPO UGTU-UPI (2008)
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Kalashnykova, N., Avramenko, V.V., Kalashnikov, V., Demianenko, V. (2021). On-Line Recognition of Fragments of Standard Images Distorted by Non-linear Devices and with a Presence of an Additive Impulse Interference. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_51
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