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Classification of tire tread images by using neural networks
Title Classification of tire tread images by using neural networks Author info Alžbeta Michalíková, Branislav Pažický Author Michalíková Alžbeta 1979- (95%) UMBFP05 - Katedra informatiky
Co-authors Pažický Branislav (5%)
Action Source document IEEE 15th International Scientific Conference on Informatics : informatics 2019 : proceedings, 20.-22.11.2019, Poprad. Pp. 283-287. - New York : Institute of Electrical and Electronics Engineers, 2019 ; IEEE International Scientific Conference on Informatics Informatics 2019 Note Príspevok je evidovaný v databáze WOS. Príspevok je evidovaný v databáze Scopus Keywords neurónové siete - neural networks classification analytical processing Form. Descr. príspevky v zborníku - proceedings papers Language English Country United States of America Annotation Image classification represents a complex problem which occurs in variety of science and real life applications. Neural network is one of the excellent tools which could be used to solve this problem. The research presented in this paper is motivated by several consultations with active crime scene investigators. During these consultations, it became apparent, that criminology department is in critical need of advanced software for tire tread print identification. The current software used by crime scene investigators is outdated, time demanding and hard to work with. Application for tire tread print identification should be one of results of project created on the basis of our work with criminology department. This paper presents one of the first steps of this identification tire tread position identification. Public work category AFD No. of Archival Copy 49889 Repercussion category ZHANG, Fenglei - LI, Da - LI, Shenghua - GUAN, Weili - LIU, Meng. A lightweight tire tread image classification network. In 2022 IEEE international conference on visual communications and image processing : VCIP 2022, Suzhou, 13rd-16th December 2022. Piscataway : Institute of electrical and electronics engineers, 2022. ISBN 978-166547592-1, pp. 1-5.
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