Отрывок: We used best performed optimization methods from scikit-learn to solve (5).(5) For shape comparison of estimated spectral sensitivity with ground truth (GT) sensitivity, we introduced normalized spectral recovery error: 𝑁𝑆𝐸 = 1 3 ∑ ||𝛘𝒊/𝑚𝑎𝑥(𝑿) − ?̂?𝒊/𝑚𝑎𝑥(?̂?)||2 ||𝛘𝒊/𝑚𝑎𝑥(𝑿) ||2 3 𝑖=1 ⋅ 100% (7) where ?̂? is the vector matrix of estimated ...
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dc.contributor.authorNigmatzyanov A.ru
dc.contributor.authorShepelev D. A.ru
dc.contributor.authorVasilev V.ru
dc.contributor.authorErshov E.ru
dc.contributor.authorTchobanou M.ru
dc.coverage.spatialcolor patchesru
dc.coverage.spatialquality of color reproductionru
dc.coverage.spatialspectral sensitivity estimationru
dc.coverage.spatialdynamic camera calibrationru
dc.coverage.spatialgolden setru
dc.coverage.spatialцветопередачаru
dc.coverage.spatialцветовые пятнаru
dc.coverage.spatialспектральная чувствительностьru
dc.coverage.spatialоценка спектральной чувствительностиru
dc.coverage.spatialкачество цветопередачиru
dc.coverage.spatialзолотой наборru
dc.coverage.spatialдинамическая калибровка камерыru
dc.creatorNigmatzyanov A., Shepelev D. A., Vasilev V., Ershov E. , Tchobanou M.ru
dc.date.issued2022ru
dc.identifierRU\НТБ СГАУ\491280ru
dc.identifier.citationDynamic camera spectral sensitivity estimation / A. Nigmatzyanov, D. A. Shepelev, V. Vasilev, E. Ershov, M. Tchobanou // 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. 4: Искусственный интеллект / под ред. А. В. Никонорова. - 2022. - С. 043922.ru
dc.description.abstractIn large-scale series production the time for evaluating the camera spectral sensitivity is strongly limited and measured in units of seconds because of production and economic constraints. To estimate variation of spectral sensitivity properties, manufacturers usually precisely measure only a few sensors (the golden set) and use these measurements to perform quick estimation of any other sensor in the released pack. The main drawback of this approach is that the worst color reproduction error cannot be controlled for a particular device: instability of device production process usually causes significantly different sensors, which may not be included in the golden set. In that case the camera will work with low accuracy during the lifetime. To overcome this problem, we consider anew approach to camera spectral sensitivity estimation during its operation. The main idea is based on consistency estimationof images and average scenes spectra. Users receive such a combination of data in practice, for instance mru
dc.language.isorusru
dc.relation.ispartofИнформационные технологии и нанотехнологии (ИТНТ-2022) : сб. тр. по материалам VIII Междунар. конф. и молодеж. шк. (г. Самара, 23 - 27 мая) : в 5 т. -ru
dc.sourceИнформационные технологии и нанотехнологии (ИТНТ-2022). - Т. 4 : Искусственный интеллектru
dc.titleDynamic camera spectral sensitivity estimationru
dc.typeTextru
dc.citation.spage043922ru
dc.citation.volume4ru
dc.textpartWe used best performed optimization methods from scikit-learn to solve (5).(5) For shape comparison of estimated spectral sensitivity with ground truth (GT) sensitivity, we introduced normalized spectral recovery error: 𝑁𝑆𝐸 = 1 3 ∑ ||𝛘𝒊/𝑚𝑎𝑥(𝑿) − ?̂?𝒊/𝑚𝑎𝑥(?̂?)||2 ||𝛘𝒊/𝑚𝑎𝑥(𝑿) ||2 3 𝑖=1 ⋅ 100% (7) where ?̂? is the vector matrix of estimated ...-
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