Samara University Repository
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This is an open electronic archive created to preserve and disseminate the scientific results of our university. The works of researchers, faculty members, and students are available to everyone for academic and research purposes.
The repository contains dissertations, research articles, educational and methodological materials, monographs, and regulatory documents. The collections cover a wide range of fields: from aerospace technologies, engine engineering, and computer science to history, linguistics, mathematics, and ecology. The archive brings together the scientific heritage of the university, including materials prepared during the period of Samara State Aerospace University and Samara State University.
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| Title: | Application of Renyi mutual information in stochastic referencing of multispectral and multi-temporal images |
| Keywords: | multispectral images multitemporal images objective functions correlation similarity measures evaluation algorithms Renyi information stochastic algorithms stochastic binding алгоритмы оценки информация Реньи меры корреляционного подобия стохастическая привязка стохастические алгоритмы мультивременные изображения мультиспектральные изображения |
| Issue Date: | 2022 |
| Citation: | Tashlinskii, A. Application of Renyi mutual information in stochastic referencing of multispectral and multi-temporal images / A. Tashlinskii, R. Ibragimov, G. Safina // Информационные технологии и нанотехнологии (ИТНТ-2022) : сб. тр. по материалам VIII Междунар. конф. и молодеж. шк. (г. Самара, 23 - 27 мая) : в 5 т. / М-во науки и образования Рос. Федерации, Самар. нац. исслед. ун-т им. С. П. Королева (Самар. ун-т), Ин-т систем обраб. изобр. РАН - фил. ФНИЦ "Кристаллография и фотоника" РАН. - Самара : Изд-во Самар. ун-та, 2022Т. 3: Распознавание, обработка и анализ изображений / под ред. В. В. Мясникова. - 2022. - С. 030912. |
| URI: | http://repo.ssau.ru/jspui/handle/123456789/12314 |
| Appears in Collections: | Информационные технологии и нанотехнологии |
| File | Size | Format | |
|---|---|---|---|
| 978-5-7883-1791-5_2022-030912.pdf | 745.68 kB | Adobe PDF | View/Open |
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