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Vestnik Tomskogo gosudarstvennogo universiteta Upravlenie vychislitel naya tekhnika i informatika
Article . 2020 . Peer-reviewed
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Vestnik Tomskogo gosudarstvennogo universiteta Upravlenie vychislitel naya tekhnika i informatika
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https://dx.doi.org/10.17223/19...
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Semantic segmentation algorithms of the Earth's surface pictures based on neural network methods

Алгоритмы семантической сегментации снимков земной поверхности на основе нейронных сетей
descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Jun 2020 Russian Federation Publisher:Tomsk State UniversityJournal:Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel'naya tekhnika i informatika (issn: 1998-8605, Copyright policy )
Authors: Druki, Alexey Alexeevich; Spitsyn, Vladimir Grigorievich; Arkalykov, Erbolat Usenovich;

doi: 10.17223/19988605/51/8

Semantic segmentation algorithms of the Earth's surface pictures based on neural network methods

Abstract

The aim of the work is to develop algorithms that solve the problem of semantic segmentation of images. A convolutional neural network with an original architecture was developed. Performing a software implementation of the algorithm, which allows to build a map of segmented objects of a different class. A comparison of the results of the proposed algorithm with existing analogues is presented

Country
Russian Federation
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Keywords

семантическая сегментация, компьютерное зрение, обработка изображений, искусственные нейронные сети

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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