<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper.</p>
</abstract>

This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D image plane technology. Using a weight-sharing network and convolution operation, sports injury features are extracted and fused, and a fusion detection framework for sports injury image features is created. Data analysis was conducted using an example to verify the detection framework’s effectiveness. The results show that the peak signal-to-noise ratio of acquiring athletes’ sports injury region imaging by the algorithm in this paper is 43 dB, and the average detection time is 5.91 s. The error control for sports injury detection was reduced from 0.102 to 0.011 after 600 iterations of the algorithm in this paper.

Multimedia intelligent 3D images for automatic detection of sports injuries

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Multimedia intelligent 3D images for automatic detection of sports injuries"}

This paper uses the types and causes of sports injuries as the entry point to fuse 2D dynamic MRI with a 3D static motion for image alignment in multimedia 3D...

Multimedia intelligent 3D images for automatic detection of sports injuries

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 14 nov 2022

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00882

Parole chiave
Sports injuries, Multimedia, Intelligent 3D images, Weight sharing network, Image feature fusion

© 2023 Hongyu Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Multimedia intelligent 3D images for automatic detection of sports injuries

Pubblicato online: 30 ott 2023

Pagine: -

Ricevuto: 14 nov 2022

Accettato: 10 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00882

Parole chiaveSports injuries, Multimedia, Intelligent 3D images, Weight sharing network, Image feature fusion

© 2023 Hongyu Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Sports injuries, Multimedia, Intelligent 3D images, Weight sharing network, Image feature fusion