Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Ultra-high PDCR(>10<sup>9</sup>) of vacuum-UV photodetector based on Al-doped Ga<sub>2</sub>O<sub>3</sub>microbelts.

Nanotechnology·2024
Same author

Development of a prognostic nomogram for advanced non-small cell lung cancer using clinical characteristics.

iScience·2024
Same author

Benign pure androgen-secreting adrenal tumor misdiagnosed as adrenocortical carcinoma on <sup>18</sup>F-FDG PET-CT: a rare case report.

Endocrine·2024
Same author

Theoretical Study on the Thermal Decomposition Mechanism of Fe(EDTA)<sup>-</sup> and Fe(EDTMP)<sup></sup>.

Molecules (Basel, Switzerland)·2024
Same author

Green modification of biochar with poly(aspartic acid) enhances the remediation of Cd and Pb in water and soil.

Journal of environmental management·2024
Same author

Enhanced plasmonic scattering imaging via deep learning-based super-resolution reconstruction for exosome imaging.

Analytical and bioanalytical chemistry·2024

相关实验视频

Updated: Jul 8, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

基于快速视觉细分和3D图像处理的新媒体艺术设计.

Zhan Wang1

  • 1Sanmenxia Polytechnic, Sanmenxia, China.

PeerJ. Computer science
|December 11, 2023
PubMed
概括

本研究介绍了一种新的方法来分析艺术图像,使用增强的U-net细分框架和用于3D重建的表面提取. 该方法在对复杂的艺术风格进行细分和重建,以实现新媒体艺术创作方面实现了高准确性.

科学领域:

  • 计算机视觉 计算机视觉
  • 数字艺术 数字艺术 数字艺术
  • 新媒体艺术新媒体艺术

背景情况:

  • 当代新媒体艺术在从复杂的图像中提取风格方面面临着挑战.
  • 现有的方法难以准确地细分和重建艺术元素.

研究的目的:

  • 开发一种创新的方法来从复杂的图像中提取艺术风格和作品.
  • 为了实现精确的3D重建艺术元素的新媒体艺术.

主要方法:

  • 利用一个增强的U-net细分框架进行图像分区.
  • 集成的表面提取和图像重建算法.
  • 开发了一种从细分数据中生成3D艺术模型的方法.

主要成果:

  • 实现了0.939的欧盟平均交叉点 (MIoU),以获得细分精度.
  • 在重建中获得了38.16的峰值信号噪声比和0.9808的结构相似性.
  • 展示了艺术特征的有效细分和3D重建.

结论:

  • 提出的方法论准确地细分和重建艺术图像中的语义复杂性.
  • 这种方法增强了艺术家辨别艺术范式的能力,并创造了复杂的新媒体艺术.
关键词:
3D重建重建的3D重建图像细分 图像细分 图像细分新媒体艺术 形象设计 设计在U-net中,U-net是指U-net网络.

更多相关视频

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K

相关实验视频

Last Updated: Jul 8, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

15.7K
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

12.8K
  • U-net细分和表面提取的融合为数字艺术分析和创作提供了一个强大的工具.