ANN MODEL FOR 3D FEATURE STABILIZATION

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Выпуск: Том 3 № 4 (2024): Прикладные науки в современном мире: проблемы и решения
Раздел: Статьи
Опубликован: апр 5, 2024

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Mirzayan Kamilov
Academician of the Academy of Sciences of Uzbekistan, Doctor of Technical Sciences, Professor, Digital Technologies and Artificial Intelligence Research Institute, Tashkent, Uzbekistan
info@in-academy.uz
Khabibullo Nosirov
Professor, Department of TV and Radio Broadcasting Systems, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan
info@in-academy.uz
Shohruh Begmatov
Associate Professor, Department of TV and Radio Broadcasting Systems, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan
info@in-academy.uz
Mukhriddin Arabboev
Associate Professor, Department of TV and Radio Broadcasting Systems, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent, Uzbekistan
info@in-academy.uz

Аннотация:

 


Three-dimensional (3D) feature stabilization is a crucial aspect in various fields such as computer vision, robotics, and augmented reality. It is essential to maintain the stability of identified features across frames for accurate analysis and reliable performance. In this paper, we propose an Artificial Neural Network (ANN) model designed specifically for 3D feature stabilization tasks. Our model uses the inherent capacity of neural networks to learn complex patterns and relationships within sequential data to effectively stabilize 3D features across consecutive frames.

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Как цитировать:

Kamilov, M. ., Nosirov, K., Begmatov, S., & Arabboev, M. . (2024). ANN MODEL FOR 3D FEATURE STABILIZATION. Прикладные науки в современном мире: проблемы и решения, 3(4), 4–8. извлечено от https://www.in-academy.uz/index.php/zdaf/article/view/29694

Библиографические ссылки:

F. Liu, M. Gleicher, H. Jin, and A. Agarwala, “Content-preserving warps for 3D video stabilization,” ACM Trans. Graph., vol. 28, no. 3, 2009.

F. Liu, M. Gleicher, J. Wang, H. Jin, and A. Agarwala, “Subspace video stabilization,” ACM Trans. Graph., vol. 30, no. 1, 2011.

Y. S. Wang, F. Liu, P. S. Hsu, and T. Y. Lee, “Spatially and temporally optimized video stabilization,” IEEE Trans. Vis. Comput. Graph., vol. 19, no. 8, pp. 1354–1361, 2013.

F. Liu, Y. Niu, and H. Jin, “Joint subspace stabilization for stereoscopic video,” Proc. IEEE Int. Conf. Comput. Vis., pp. 73–80, 2013.

C. Tang, O. Wang, L. I. U. Feng, and T. A. N. Ping, “Joint stabilization and direction of 360◦ videos,” ACM Trans. Graph., vol. 38, no. 2, 2019.

M. Rikhsivoev et al., “Working Principles of Multi-Frame Image Super- Resolution,” Int. Sci. Tech. Conf. “DIGITAL Technol. Probl. Solut. Pract. Implement. SPHERES” April 27-28, 2023, pp. 244–249, 2023.

M. Arabboev et al., “Development of a Novel Method for Image Resizing Using Artificial Neural Network,” in In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741.

M. Arabboev, S. Begmatov, K. Nosirov, J. C. Chedjou, and K. Kyamakya, “Development of a novel method of adaptive image interpolation for image resizing using artificial intelligence,” in IVUS 2022: 27th International Conference on Information Technology, pp. 32–38