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dc.rights.licenseVisos teisės saugomos / All rights reserveden_US
dc.contributor.authorSmelyakov, Kirill
dc.contributor.authorChupryna, Anastasiya
dc.contributor.authorPonomarenko, Oleksandr
dc.contributor.authorKolisnyk, Maksym
dc.date.accessioned2025-12-15T12:53:06Z
dc.date.available2025-12-15T12:53:06Z
dc.date.issued2020
dc.identifier.isbn9781728197807en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/159553
dc.description.abstractNowadays, modern big data warehouses require the development of effective management algorithms. For large image storages first of all it's important to develop effective algorithms for comparing and searching a similar image from an image, taking into account their possible geometric transformations. In this regard, one of the most promising is the approach based on the use of invariant Local Feature Detectors. The work is carried out experimental research of such detectors’ effectiveness for Search by Image Engine in large image storages.en_US
dc.format.extent4 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/159395en_US
dc.source.urihttps://ieeexplore.ieee.org/document/9108884en_US
dc.subjectimageen_US
dc.subjectsearch engineen_US
dc.subjectlocal feature detectoren_US
dc.subjectimage transformationen_US
dc.subjectefficiency estimationen_US
dc.titleSearch by Image Engine using Local Feature Detectorsen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.issued2020-06-05
dcterms.references24en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionKharkiv National University of Radio Electronicsen_US
dcterms.sourcetitle2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 30, 2020, Vilnius, Lithuaniaen_US
dc.identifier.eisbn9781728197791en_US
dc.publisher.nameIEEEen_US
dc.publisher.countryUnited States of Americaen_US
dc.publisher.cityNew Yorken_US
dc.identifier.doihttps://doi.org/10.1109/eStream50540.2020.9108884en_US


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