Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites. / Романов, Матвей Александрович; Вохминцев, Александр; Христодуло, Ольга.
Analysis of Images, Social Networks and Texts (AIST 2023). Springer Nature, 2024. стр. 294-308 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 14486 LNCS).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
}
TY - GEN
T1 - Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites
AU - Романов, Матвей Александрович
AU - Вохминцев, Александр
AU - Христодуло, Ольга
N1 - Vokhmintcev, A. & Khristodulo, O. & Melnikov, A. & Romanov, M. Application of Dynamic Graph CNN* and FICP for Detection and Research Archaeology Sites. Proceedings of the 2023 International Conference on Analysis of Images, Social Networks and Texts (Yerevan, Armenia, 28-30 September 2023). AIST 2023. P. 294-308. DOI: 10.1007/978-3-031-54534-4_21. (Scopus-Q2, Web of science-Q2) https://link.springer.com/chapter/10.1007/978-3-031-54534-4_21
PY - 2024/3/12
Y1 - 2024/3/12
N2 - The paper proposes a methodology for solving the task of accurate semantic classification of 3D data using a combination of 2D and 3D methods based on the YOLO detector and the modified DGCNN network. The methodology is tested on the example of the problem of classification of large-scale geospatial objects, such as digital relief models of archaeological sites. A method for accurate registration of objects (FCIP) in the class of affine transformations using geometric and color features was proposed. The results of computer modeling of the proposed methodology based on FICP+DGCNN*+YOLO were presented and discussed. The methodology has theoretical and applied significance not only for the decryption and research of archaeological sites, but also for many applications of digital information processing and robotics in general.
AB - The paper proposes a methodology for solving the task of accurate semantic classification of 3D data using a combination of 2D and 3D methods based on the YOLO detector and the modified DGCNN network. The methodology is tested on the example of the problem of classification of large-scale geospatial objects, such as digital relief models of archaeological sites. A method for accurate registration of objects (FCIP) in the class of affine transformations using geometric and color features was proposed. The results of computer modeling of the proposed methodology based on FICP+DGCNN*+YOLO were presented and discussed. The methodology has theoretical and applied significance not only for the decryption and research of archaeological sites, but also for many applications of digital information processing and robotics in general.
KW - 3D semantic segmentation and classification methods
KW - DGCNN
KW - DTM
KW - ICP
KW - object detector
UR - https://www.mendeley.com/catalogue/407909e0-73cb-3d70-9842-7abe8a0a51cc/
U2 - 10.1007/978-3-031-54534-4_21
DO - 10.1007/978-3-031-54534-4_21
M3 - Conference contribution
SN - 978-3-031-54533-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 308
BT - Analysis of Images, Social Networks and Texts (AIST 2023)
PB - Springer Nature
Y2 - 28 September 2023 through 30 September 2023
ER -
ID: 124116723