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Recognition Algorithm for Biological and Criminalistics Objects

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dc.contributor.authorKulik, S. D.
dc.contributor.authorShtanko, A. N.
dc.contributor.authorКулик, Сергей Дмитриевич
dc.date.accessioned2024-11-25T14:48:38Z
dc.date.available2024-11-25T14:48:38Z
dc.date.issued2020
dc.description.abstract© 2020, Springer Nature Switzerland AG.This paper describes the results of a work to develop an algorithm for analyzing images of embossed impressions in paper documents under oblique lighting. The described algorithm could also be used for recognition of similarly-structured objects, for example, some of biological structures. This type of analysis is necessary during forensic analysis of certain security features of paper documents. Part of this analysis is determining to which category new, uncategorized impression belongs to. This research explores the potential for automation of this task using neural networks. The core element of the algorithm is a neural network which determines the similarity between two embossed impressions. The paper describes the structure of the algorithm, a method for creating an image database for training and testing, as well as testing results for proposed algorithm.
dc.format.extentС. 283-294
dc.identifier.citationKulik, S. D. Recognition Algorithm for Biological and Criminalistics Objects / Kulik, S.D., Shtanko, A.N. // Advances in Intelligent Systems and Computing. - 2020. - 948. - P. 283-294. - 10.1007/978-3-030-25719-4_36
dc.identifier.doi10.1007/978-3-030-25719-4_36
dc.identifier.urihttps://www.doi.org/10.1007/978-3-030-25719-4_36
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85070222017&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/20012
dc.relation.ispartofAdvances in Intelligent Systems and Computing
dc.titleRecognition Algorithm for Biological and Criminalistics Objects
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume948
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relation.isAuthorOfPublication.latestForDiscovery7ce44b2d-ec0b-42b1-beca-1477fbb1b638
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