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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.9K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
6.9K

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

Toward leveraging intrinsic point cloud features in 3D adversarial attacks.

PloS one·2026
Same author

LPF-Defense: 3D adversarial defense based on frequency analysis.

PloS one·2023
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Jun 11, 2025

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

对于无监督图像实例分割的深层光谱改进.

Farnoosh Arefi1, Amir M Mansourian1, Shohreh Kasaei1

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

PloS one
|October 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了深层光谱实例细分的新方法,通过减少噪音特征通道来提高准确性,并提出了强大的相似度指标. 这些技术提高了基准数据集的性能.

更多相关视频

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K
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

8.0K

相关实验视频

Last Updated: Jun 11, 2025

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
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K
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

8.0K

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 图像处理 图像处理

背景情况:

  • 深度光谱方法正在获得图像细分的吸引力,适应传统的光谱技术.
  • 使用深度光谱方法进行实例细分是未经探索的,面临特征地图通道噪声的挑战.

研究的目的:

  • 在深层光谱框架内增强实例细分性能.
  • 在自我监督的功能地图中解决噪音和信息不足的频道问题.
  • 提出一个更合适的相似度指标,比如细分与点积相比.

主要方法:

  • 引入噪声通道减少 (NCR) 和基于偏差的通道减少 (DCR) 模块,用于特征地图通道修剪.
  • 提出了一个新的相似度指标,Bray-Curtis对Chebyshev (BoC),用于亲和力矩阵的构建.
  • 在Youtube-VIS 2019和OVIS数据集上评估的方法.

主要成果:

  • NCR和DCR有效地减少了噪音和不信息的频道,提高了细分的准确性.
  • 该BoC指标表现出优越的性能比点积,例如细分.
  • 观察到平均交叉点在整个欧盟 (mIoU) 和实例细分质量的显著改善.

结论:

  • 拟议的通道缩小技术和BoC相似度量大大提升了深层光谱实例细分.
  • 这些方法为实例细分任务提供了更强大,更准确的方法.
  • 这些发现为更有效的深光谱图像分析铺平了道路.