Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: IV International Conference on Advances in Materials, Earth Science and Technology (CAMSTech-IV-2024); Bukhara; Bukhara

Год издания: 2026

Идентификатор DOI: 10.1063/5.0322210

Аннотация: This article examines the primary computer vision methods of the OpenCV library designed for automated object detection, recognition, and classification in digital images, aimed at providing information for decision-making and subsequent processing. Based on the analysis of image segmentation methods and algorithms, feature extractПоказать полностьюion, object detection and recognition, as well as clustering, the authors propose solutions to optimize their performance and adapt them to the applied task of digital restoration of objects of historical and cultural heritage.

Журнал: Proceedings of the IV International Conference on Advances in Materials, Earth Science and Technology (CAMSTech-IV-2024)

Номера страниц: 60013

Место издания: Melville

• Grace Amelia (Krasnoyarsk State Agrarian University)
• Kovalev Igor (Navoi State University of Mining and Technologies)
• Voroshilova Anna (Siberian Federal University)
• Kadirov Yorkin (Navoi State University of Mining and Technologies)
• Lukyanov Kirill (Siberian Federal University)

• Ядро РИНЦ (eLIBRARY.RU)