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

Continuous-surface 3D reconstruction from kilometer-range single-photon LiDAR using score-based priors.

Scientific reports·2026
Same author

Human activity recognition at a kilometer range using single-photon LiDAR.

Optics express·2026
Same author

A Study of the Avalanche Multiplication and Excess Noise in Al <sub><i>x</i></sub> In<sub>1-<i>x</i></sub> As<sub>γ</sub>Sb<sub>1‑</sub> <sub>γ</sub> Avalanche Photodiodes Lattice-Matched to GaSb.

ACS photonics·2026
Same author

Underwater 3D imaging using a single-photon avalanche diode detector array with multi-event time-to-digital conversion.

Optics express·2026
Same author

Doctors are at high risk of "complexity fatigue".

BMJ (Clinical research ed.)·2026
Same author

Bayesian Multifractal Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

相关实验视频

Updated: Jul 22, 2025

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K

用单光子LiDAR数据进行3D目标检测和光谱分类.

Mohamed Amir Alaa Belmekki, Jonathan Leach, Rachael Tobin

    Optics express
    |July 21, 2023
    PubMed
    概括

    这项研究引入了用于3D单光子LiDAR成像的新型多尺度方法,显著减少数据量并改善目标检测. 该方法使无背景3D表面重建和光谱分类成为可能,即使在具有挑战性的条件下.

    科学领域:

    • 光子学和成像技术
    • 计算成像技术的成像
    • 遥感 遥感 遥感 遥感

    背景情况:

    • 3D单光子LiDAR成像至关重要,但面临的挑战是低信号噪声比和高数据量.
    • 通过遮光剂或高环境光的成像降低了数据质量,并使分析复杂化.

    研究的目的:

    • 从光子计时直方图表开发一种多尺度的方法,以高效地从光子计时直方图进行3D表面检测.
    • 为了使多光谱单光子LiDAR数据的无背景3D重建和光谱分类.

    主要方法:

    • 一种用于光子计时直方图的多尺度处理方法,以减少数据量.
    • 一个层次化的贝叶斯模型用于3D重建和光谱分类.
    • 坐标梯度下降算法用于参数估计,促进空间相关性.

    主要成果:

    • 显著减少3D单光子LiDAR的数据量.
    • 为深度和反射性推断生成无背景表面.
    • 与现有方法相比,对模拟和真实数据的证明好处.

    结论:

    • 拟议的多尺度方法增强了3D单光子LiDAR数据分析.

    更多相关视频

    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
    09:46

    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

    Published on: April 28, 2022

    4.0K
    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K

    相关实验视频

    Last Updated: Jul 22, 2025

    A Protocol for Real-time 3D Single Particle Tracking
    10:16

    A Protocol for Real-time 3D Single Particle Tracking

    Published on: January 3, 2018

    15.0K
    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging
    09:46

    Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

    Published on: April 28, 2022

    4.0K
    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    00:07

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.1K
  • 即使在杂或复杂的环境中,也可以实现有效的目标检测和重建.
  • 该方法为多光谱单光子LiDAR数据处理提供了强大的解决方案.