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

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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

Lighting effects on optimal facial regions for remote heart rate measurement.

NPJ cardiovascular health·2026
Same author

Reconfigurable assembly, disassembly, and self-propulsion of microparticles via optoelectronic tweezers.

Microsystems & nanoengineering·2026
Same author

Image Restoration Learning via Noisy Supervision in Fourier Domain.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

EventTracer: Fast Path Tracing-Based Event Stream Rendering.

IEEE transactions on visualization and computer graphics·2026
Same author

Seasonal Divergence between Microbiomes on Microplastics and Natural Particles Increases with Rising Water Temperatures in Urban Rivers.

Environmental science & technology·2026
Same author

Roadmap of remote photoplethysmography from heart rate measurement toward clinical translation.

NPJ digital medicine·2026

相关实验视频

Updated: May 18, 2026

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

13.6K

超快的动态缺陷检查与计算神经形态成像.

Shuo Zhu1, Qianfeng Yin2,3, Chutian Wang1

  • 1Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, 999077, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 23, 2025
PubMed
概括

计算神经形态成像 (CNI) 为制造业提供超快,高动态范围的表面缺陷检查. 这种基于事件的传感器方法克服了传统摄像机的局限性,在具有挑战性的工业环境中实现实时诊断.

关键词:
计算神经形态成像计算神经形态成像缺陷检查检查检查缺陷检查检查检查检查检查检查检查检查检查超快的动态环境 超快的动态环境

更多相关视频

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

3.3K
Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice
08:26

Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice

Published on: August 23, 2022

2.9K

相关实验视频

Last Updated: May 18, 2026

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
10:45

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

13.6K
Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

3.3K
Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice
08:26

Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice

Published on: August 23, 2022

2.9K

科学领域:

  • 先进制造先进制造 制造先进制造
  • 计算机视觉 计算机视觉
  • 传感器技术 传感器技术

背景情况:

  • 传统的工业摄像机在非理想条件下与延迟和有限的动态范围作斗争.
  • 目前的缺陷检查方法对振动敏感,需要控制的环境.
  • 限制阻碍高需求制造业的效率和质量保证.

研究的目的:

  • 开发一种用于超快速动态表面缺陷检测的新型检查范式.
  • 利用基于事件的传感器来增强缺陷检查能力.
  • 解决工业质量控制中传统成像技术的局限性.

主要方法:

  • 开发了一种计算神经形象成像 (CNI) 方法.
  • 使用基于事件的传感器,具有高时间分辨率和动态范围.
  • 实现事件驱动的数据处理,用于实时分析和可视化.

主要成果:

  • 实现了超快速检查,在快速运动中提升了300倍的采样时间.
  • 在不同的照明条件下,已证明其动态范围超过1万倍.
  • 启用了使用事件驱动数据的直接边缘检测和缺陷可视化.
  • 通过使用振动来展示增强的结构缺陷检测.

结论:

  • CNI提供了一种具有成本效益的解决方案,用于超快速,高动态范围的缺陷检查.
  • 这种方法最大限度地减少了实时诊断的感知和计算延迟.
  • CNI为各种工业应用和充满挑战的环境提供了优势.
  • 这种方法在精密制造中推进了实时检查和智能诊断.