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<p>As a carrier of human cultural heritage, it is of great practical significance to apply VR technology to the construction of modern digital museums. This paper combines artificial intelligence and SNN algorithms for image segmentation and proposes the overall framework of ‘embedded image acquisition and SNN segmentation processing’. This system is utilized to improve the picture clarity and delay efficiency of VR technology and reduce the vertigo feeling of the user experience. The VR environment of the digital museum is designed according to the user’s intentions. Finally, the effectiveness of the designed museum VR environment is verified by evaluating the comfort, interaction, and emotional experience of the VR museum. The experimental results show that in terms of comfort experience, the SSQ scale score of the VR museum experience based on the embedded image processing is 7.1, which is nearly double the score of the original VR museum experience. In terms of interaction and emotional experience, the overall satisfaction score of the VR environment design of the M museum was 9.4. The two aspects of emotional experience and cultural communication effects were evaluated as 4.27 and 4.05, respectively. It indicates that the user satisfaction of the VR environment design of the AI and digital museum based on the embedded image processing is high, and that this VR environment design can effectively improve the communication of the museum.</p>
</abstract>

As a carrier of human cultural heritage, it is of great practical significance to apply VR technology to the construction of modern digital museums. This paper combines artificial intelligence and SNN algorithms for image segmentation and proposes the overall framework of ‘embedded image acquisition and SNN segmentation processing’. This system is utilized to improve the picture clarity and delay efficiency of VR technology and reduce the vertigo feeling of the user experience. The VR environment of the digital museum is designed according to the user’s intentions. Finally, the effectiveness of the designed museum VR environment is verified by evaluating the comfort, interaction, and emotional experience of the VR museum. The experimental results show that in terms of comfort experience, the SSQ scale score of the VR museum experience based on the embedded image processing is 7.1, which is nearly double the score of the original VR museum experience. In terms of interaction and emotional experience, the overall satisfaction score of the VR environment design of the M museum was 9.4. The two aspects of emotional experience and cultural communication effects were evaluated as 4.27 and 4.05, respectively. It indicates that the user satisfaction of the VR environment design of the AI and digital museum based on the embedded image processing is high, and that this VR environment design can effectively improve the communication of the museum.

Artificial Intelligence and Digital Museum VR Environment Design Based on Embedded Image Processing

Chongqing Technology and Business University

Applied Mathematics and Nonlinear Sciences

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

As a carrier of human cultural heritage, it is of great practical significance to apply VR technology to the construction of modern digital museums. This paper...

Artificial Intelligence and Digital Museum VR Environment Design Based on Embedded Image Processing

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 24. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0116

Schlüsselwörter
SNN algorithm, VR design, Artificial intelligence, Digital museum, Image processing

© 2024 Jing Luo et al., published by Sciendo

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

Artificial Intelligence and Digital Museum VR Environment Design Based on Embedded Image Processing

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 24. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0116

SchlüsselwörterSNN algorithm, VR design, Artificial intelligence, Digital museum, Image processing

© 2024 Jing Luo et al., published by Sciendo

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

Schlüsselwörter
SNN algorithm, VR design, Artificial intelligence, Digital museum, Image processing