Application of a Convolutional Neural Network with a Module of Elementary Graphic Primitive Classifiers in the Problems of Recognition of Drawing Documentation and Transformation of 2d to 3d Models

Khorolska, K. and Skladannyi, Pavlo and Sokolov, Volodymyr and Korshun, Natalia and Bebeshko, B. and Lakhno, V. and Zhumadilova, M. (2022) Application of a Convolutional Neural Network with a Module of Elementary Graphic Primitive Classifiers in the Problems of Recognition of Drawing Documentation and Transformation of 2d to 3d Models Journal of Theoretical and Applied Information Technology, 100 (24). pp. 7426-7437. ISSN 1817-3195

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Abstract

his paper presents the results of the research related to the design of a convolutional neural network with a module of graphic primitives elementary classifiers (EC) in the tasks of drawing documentation recognition and transformation of the 2D into 3D models. An architecture of a convolutional neural network with an elementary classifiers module of graphic primitives was proposed for solving the drawing recognition and 2 → 3 transformation problem. A graphic image classifier model based on covered classes and elementary primitive classifiers has been developed to increase the effectiveness of CNN training.

Item Type: Article
Uncontrolled Keywords: Decision Support System; Information Protection; Information Security; Infrastructure Management; Organizational and Economic Support; Risk Minimization
Subjects: Це архівна тематика Київського університету імені Бориса Грінченка > Статті у наукометричних базах > Scopus
Divisions: Це архівні підрозділи Київського університету імені Бориса Грінченка > Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки імені професора Володимира Бурячка
Depositing User: Павло Миколайович Складанний
Date Deposited: 17 Jul 2023 07:03
Last Modified: 17 Jul 2023 07:03
URI: https://elibrary.kubg.edu.ua/id/eprint/43769

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