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Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art

As technology changes, virtual reality generates realistic images through computer graphics and provides users with an immersive experience through various interactive means. In the context of digitalization, the application of VR for digital media art creation becomes a normalized method. Today...

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Detalles Bibliográficos
Autor principal: Wu, Yunxuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385317/
https://www.ncbi.nlm.nih.gov/pubmed/35990155
http://dx.doi.org/10.1155/2022/3781750
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author Wu, Yunxuan
author_facet Wu, Yunxuan
author_sort Wu, Yunxuan
collection PubMed
description As technology changes, virtual reality generates realistic images through computer graphics and provides users with an immersive experience through various interactive means. In the context of digitalization, the application of VR for digital media art creation becomes a normalized method. Today's digital media art creation is closely related to vigorous technological innovation behind it, so the influence of modern technology is inevitable. Virtual reality and artificial intelligence have gradually become the main technical means in line with the development aim for digital media art creation. This work proposes an art object detection method AODNET in virtual reality digital media art creation with AI. Aiming at the particularity of object detection in this direction, an art object detection strategy based on residual network and clustering idea is proposed. First of all, it uses ResNet50 as backbone, which deepens network depth and improves the model feature extraction ability. Second, it uses the K-means++ algorithm to perform clustering statistics on the size of the real annotated boxes in the dataset to obtain appropriate hyperparameters for preset candidate boxes, which enhances the tolerance of the algorithm to the target size. Third, it replaces the ROI pooling algorithm with ROI align to eliminate the error caused by the quantization operation on the characteristics of the candidate region. Fourth, to reduce the missed detection rate of overlapping targets, soft-NMS algorithm is used instead of the NMS algorithm to post-process the candidate boxes. Finally, this work conducts extensive experiments to verify the superiority of AODNET for object detection in virtual reality digital media art creation.
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spelling pubmed-93853172022-08-18 Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art Wu, Yunxuan Comput Intell Neurosci Research Article As technology changes, virtual reality generates realistic images through computer graphics and provides users with an immersive experience through various interactive means. In the context of digitalization, the application of VR for digital media art creation becomes a normalized method. Today's digital media art creation is closely related to vigorous technological innovation behind it, so the influence of modern technology is inevitable. Virtual reality and artificial intelligence have gradually become the main technical means in line with the development aim for digital media art creation. This work proposes an art object detection method AODNET in virtual reality digital media art creation with AI. Aiming at the particularity of object detection in this direction, an art object detection strategy based on residual network and clustering idea is proposed. First of all, it uses ResNet50 as backbone, which deepens network depth and improves the model feature extraction ability. Second, it uses the K-means++ algorithm to perform clustering statistics on the size of the real annotated boxes in the dataset to obtain appropriate hyperparameters for preset candidate boxes, which enhances the tolerance of the algorithm to the target size. Third, it replaces the ROI pooling algorithm with ROI align to eliminate the error caused by the quantization operation on the characteristics of the candidate region. Fourth, to reduce the missed detection rate of overlapping targets, soft-NMS algorithm is used instead of the NMS algorithm to post-process the candidate boxes. Finally, this work conducts extensive experiments to verify the superiority of AODNET for object detection in virtual reality digital media art creation. Hindawi 2022-08-10 /pmc/articles/PMC9385317/ /pubmed/35990155 http://dx.doi.org/10.1155/2022/3781750 Text en Copyright © 2022 Yunxuan Wu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Yunxuan
Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art
title Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art
title_full Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art
title_fullStr Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art
title_full_unstemmed Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art
title_short Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art
title_sort application of artificial intelligence within virtual reality for production of digital media art
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385317/
https://www.ncbi.nlm.nih.gov/pubmed/35990155
http://dx.doi.org/10.1155/2022/3781750
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