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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.7K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.7K
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

907
Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
907

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

The role of cGAS-STING pathway in the development of radiation-induced lung injury.

Journal of cancer research and clinical oncology·2025
Same author

Microbial manganese redox cycling drives co-removal of nitrate and ammonium.

Journal of environmental management·2025
Same author

Atomically Dispersed Ta-O-Co Sites Capable of Mitigating Side Reaction Occurrence for Stable Lithium-Oxygen Batteries.

Journal of the American Chemical Society·2025
Same author

Cortical Gyrification and Cognitive Decline in the Human Brain With Type 2 Diabetes Mellitus.

Brain and behavior·2025
Same author

Fluorine Doping-Assisted Reconstruction of Isolated Cu Sites for CO<sub>2</sub> Electroreduction Toward Multicarbon Products.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Repurposing of phosphodiesterase-5 inhibitor sildenafil as a therapeutic agent to prevent gastric cancer growth through suppressing c-MYC stability for IL-6 transcription.

Communications biology·2025

相关实验视频

Updated: May 2, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

图像理解点云:通过关联学习进行弱监督的3D语义细分.

Tianfang Sun, Zhizhong Zhang, Xin Tan

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 7, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种新的弱监督的3D语义细分方法,使用未标记的图像来提高性能. 该方法取得了最先进的结果,甚至超过了使用最小标签的完全监督方法.

    更多相关视频

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    533

    相关实验视频

    Last Updated: May 2, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    9.0K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    533

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 3D数据分析 3D数据分析

    背景情况:

    • 对点云的弱监督的语义细分旨在减少标签工作.
    • 现有的方法往往忽略了LiDAR场景中的图像中的有价值的补充信息.
    • 自主训练和伪标签是常见的,但可以忽略跨模式数据.

    研究的目的:

    • 开发一种用于3D语义细分的新型跨模式弱监督方法.
    • 利用未标记的图像数据来提高点云细分性能.
    • 以最小的标记数据实现高精度,接近完全监督的性能.

    主要方法:

    • 一个双分支网络,具有2D到3D知识转移的积极标签策略.
    • 一个跨模式的自我训练框架,具有代的参数更新和伪标签估计.
    • 使用3D点和2D超像素之间的循环一致性进行交叉模式关联学习.
    • 一个伪标签自我纠正机制,在训练期间过噪音标签.

    主要成果:

    • 拟议的方法有效地利用未标记的图像数据进行3D点云细分.
    • 跨模式学习显著提高了从补充数据的监督采矿.
    • 伪标签纠正机制提高了标签准确性和网络培训.
    • 实验结果显示,该方法的性能优于最先进的全监督方法,注释率为<1%.

    结论:

    • 将未标记的图像数据与弱监督的点云细分集成是非常有效的.
    • 拟议的跨模式方法为高效的3D语义细分提供了强大的解决方案.
    • 与使用最小标记数据的完全监督技术相比,这种方法实现了更高的性能.