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@article{ISU_2024_24_3_a10, author = {A. A. Klyachin}, title = {Extraction of features in images based on integral transformations in solving problems of classification of fragments of photographs}, journal = {Izvestiya of Saratov University. Mathematics. Mechanics. Informatics}, pages = {432--441}, publisher = {mathdoc}, volume = {24}, number = {3}, year = {2024}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/ISU_2024_24_3_a10/} }
TY - JOUR AU - A. A. Klyachin TI - Extraction of features in images based on integral transformations in solving problems of classification of fragments of photographs JO - Izvestiya of Saratov University. Mathematics. Mechanics. Informatics PY - 2024 SP - 432 EP - 441 VL - 24 IS - 3 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/ISU_2024_24_3_a10/ LA - ru ID - ISU_2024_24_3_a10 ER -
%0 Journal Article %A A. A. Klyachin %T Extraction of features in images based on integral transformations in solving problems of classification of fragments of photographs %J Izvestiya of Saratov University. Mathematics. Mechanics. Informatics %D 2024 %P 432-441 %V 24 %N 3 %I mathdoc %U http://geodesic.mathdoc.fr/item/ISU_2024_24_3_a10/ %G ru %F ISU_2024_24_3_a10
A. A. Klyachin. Extraction of features in images based on integral transformations in solving problems of classification of fragments of photographs. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 24 (2024) no. 3, pp. 432-441. http://geodesic.mathdoc.fr/item/ISU_2024_24_3_a10/
[1] R. Gonsales, R. Vuds, Tsifrovaya obrabotka izobrazhenii, Tekhnosfera, M., 2005, 1072 pp.
[2] S. M. Borzov, A. O. Potaturkin, O. I. Potaturkin, A. M. Fedotov, “Issledovanie effektivnosti klassifikatsii giperspektralnykh sputnikovykh izobrazhenii prirodnykh i antropogennykh territorii”, Avtometriya, 52:1 (2016), 3–14 | DOI | MR
[3] A. Yu. Dorogov, R. G. Kurbanov, V. V. Razin, “Bystraya klassifikatsiya JPEG-izobrazhenii”, Internet-matematika 2005. Avtomaticheskaya obrabotka veb-dannykh (Moskva, 2005), 147–172
[4] V. V. Osokin, T. D. Aipov, Z. A. Niyazova, “O klassifikatsii izobrazhenii i muzykalnykh failov”, Intellektualnye sistemy. Teoriya i prilozheniya, 19:1 (2015), 49–70, UWLELY, EDN | MR
[5] E. M. Kupenova, A. V. Kashnitskii, “Metod sluchainykh lesov v zadachakh klassifikatsii sputnikovykh snimkov”, Vestnik Tverskogo gosudarstvennogo universiteta. Seriya: Geografiya i geoekologiya, 2018, no. 3, 99–107
[6] E. F. Goncharova, A. V. Gaidel, “Metody otbora priznakov dlya zadach klassifikatsii izobrazhenii zemnoi poverkhnosti”, Informatsionnye tekhnologii i nanotekhnologii, ITNT-2017, sb. tr. III mezhdunarodnoi konferentsii i molodezhnoi shkoly, Novaya tekhnika, Samara, 2017, 535–540
[7] M. A. Turkova, A. V. Gaidel, “Korrelyatsionnye priznaki dlya klassifikatsii teksturnykh izobrazhenii”, Informatsionnye tekhnologii i nanotekhnologii, sb. tr. IV mezhdunarodnoi konferentsii i molodezhnoi shkoly, Novaya tekhnika, Samara, 2018, 595–599
[8] A. I. Chulichkov, I. V. Morozova, “Klassifikatsiya razmytykh izobrazhenii i otsenka parametrov sistemy registratsii metodami morfologicheskogo analiza”, Intellektualnye sistemy, 9:1-4 (2005), 321–344
[9] S. M. Borzov, O. I. Potaturkin, “Klassifikatsiya tipov rastitelnogo pokrova po giperspek tralnym dannym distantsionnogo zondirovaniya zemli”, Vestnik Novosibirskogo gosudar stvennogo universiteta. Seriya: Informatsionnye tekhnologii, 12:4 (2014), 13–22
[10] I. A. Pestunov, S. A. Rylov, P. V. Melnikov, “Klassifikatsiya giperspektralnykh izobrazhenii vysokogo prostranstvennogo razresheniya”, Journal of Siberian Federal University. Engineering Technologies, 11:1 (2018), 69–76 | DOI | MR
[11] D. N. Kitaev, Sravnenie svertochnoi neironnoi seti i metoda glavnykh komponent v zadache klassifikatsii teksturnykh izobrazhenii, tez. dokl., ed. A. B. Prokofev, Izd-vo Samarskogo un-ta, Samara, 2018, 88–89
[12] A. M. Golubkov, “Binarnaya klassifikatsiya izobrazhenii na primere zadachi raspoznavaniya lits”, Izvestiya SPbGETU «LETI», 2018, no. 7, 26–30
[13] E. S. Nezhevenko, A. S. Feoktistov, O. Yu. Dashevskii, “Neirosetevaya klassifikatsiya giperspektralnykh izobrazhenii na osnove preobrazovaniya Gilberta–Khuanga”, Avtometriya, 53:2 (2017), 79–84 | DOI
[14] V. D. Vaskan, “Obzor arkhitektur svertochnykh neironnykh setei dlya zadachi klassifikatsii izobrazhenii”, IT-Standart, 2021, no. 3 (28), 34–39
[15] O. P. Soldatova, A. A. Garshin, “Primenenie svertochnoi neironnoi seti dlya raspoznavaniya rukopisnykh tsifr”, Kompyuternaya optika, 34:2 (2010), 252–260
[16] Le Man Kha, “Svertochnaya neironnaya set dlya resheniya zadachi klassifikatsii”, Trudy Moskov skogo fiziko-tekhnicheskogo instituta (NIU), 8:3 (2016), 91–97
[17] I. S. Azarov, A. S. Prokopenya, “Svertochnye neironnye seti dlya raspoznavaniya izobrazhenii”, Big Data and Advanced Analytics, 2020, no. 6-1, 271–280
[18] O. S. Sikorskii, “Obzor svertochnykh neironnykh setei dlya zadachi klassifikatsii izobrazhenii”, Novye informatsionnye tekhnologii v avtomatizirovannykh sistemakh, 2017, no. 20, 37–42
[19] S. R. Deans, S. Roderick, The Radon Transform and Some of its Applications, John Wiley Sons, New York, 1983, 289 pp. | MR | Zbl
[20] V. V. Zhuk, V. F. Kuzyutin, Approksimatsiya funktsii i chislennoe integrirovanie, Izd-vo Sankt-Peterburgskogo un-ta, Sankt-Peterburg, 1995, 352 pp. | MR
[21] V. V. Voronina, A. V. Mikheev, N. G. Yarushkina, K. V. Svyatov, Teoriya i praktika mashinnogo obucheniya, ucheb. posobie, UlGTU, Ulyanovsk, 2017, 290 pp.
[22] Sholle F., Glubokoe obuchenie na Python, Biblioteka programmista, Piter, Sankt-Peterburg, 2018, 400 pp.