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

Joint feature issue in optics express and applied optics: computational optical sensing and imaging 2024.

Optics express·2025
Same author

Computational Optical Sensing and Imaging 2024: introduction.

Applied optics·2025
Same author

Probing diffusive media through speckle differencing.

Biomedical optics express·2024
Same author

Computational Optical Sensing and Imaging: introduction to the feature issue.

Applied optics·2024
Same author

Probabilistic Modeling of Multicamera Interference for Time-of-Flight Sensors.

Sensors (Basel, Switzerland)·2023
Same author

Imaging operator in indirect imaging correlography.

Optics express·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

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

立方体和圆柱形物体的尺寸仅使用噪音和部分观察到的飞行时间数据.

Bryan Rodriguez1, Prasanna Rangarajan1, Xinxiang Zhang1

  • 1Department of Electrical and Computer Engineering, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USA.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究使用超四边形拟合来改进对象尺寸与飞行时间 (ToF) 传感器,实现不到1厘米的错误立方体和圆柱体,尽管传感器的限制,如噪音和低分辨率.

关键词:
三维计量学 3D计量学通过3D扫描进行3D扫描.飞行时间传感器处理点云的点云处理.

更多相关视频

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

15.6K
Measuring the Complete-arch Distortion of an Optical Dental Impression
06:51

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

7.6K

相关实验视频

Last Updated: Jul 11, 2025

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
Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

15.6K
Measuring the Complete-arch Distortion of an Optical Dental Impression
06:51

Measuring the Complete-arch Distortion of an Optical Dental Impression

Published on: May 30, 2019

7.6K

科学领域:

  • 机器人和自动化 机器人和自动化
  • 计算机视觉 计算机视觉
  • 计量学 计量学 计量学

背景情况:

  • 飞行时间 (ToF) 传感器提供深度感应,但面临着像低分辨率,噪声和多路径干扰这样的挑战.
  • 这些限制扭曲了对象的形状和尺寸,阻碍了准确的测量应用.

研究的目的:

  • 使用ToF传感器数据,应用超方格配合框架来准确测量立方体和圆柱体物体的尺寸.
  • 评估框架在各种对象定向,地面表面和安装技术上的性能.

主要方法:

  • 利用了从ToF传感器生成的点云数据.
  • 应用一个超四边形的配套框架来模型和尺寸立方体和圆柱体的物体.
  • 研究了模拟模型的边界和镜像技术.

主要成果:

  • 在距离1.5米的距离上,在30厘米 (立方体) 和20厘米 (圆柱体) 的物体中,达到的平均尺寸误差小于1厘米.
  • 使用边界技术证明了立方体的绝对尺寸误差为4%-9%,水平圆柱体的误差为2.97%-6.61%.
  • 展示了取决于方向的性能,垂直气显示出更高的误差 (8.01%-13.13%).

结论:

  • 超四边形安装框架有效地克服了ToF传感器的局限性,用于准确的对象尺寸.
  • 该方法在不同的物体形状,方向和表面条件中提供了可量化的准确性.