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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K

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

[Analysis of clinical feature of IgG4 related disease].

Zhonghua yi xue za zhi·2016
Same author

Noninvasive measurement of renal oxygen extraction fraction under the influence of respiratory challenge.

Journal of magnetic resonance imaging : JMRI·2016
Same author

MicroRNA-103 suppresses tumor cell proliferation by targeting PDCD10 in prostate cancer.

The Prostate·2016
Same author

Clear cell carcinoma arising in previous episiotomy scar: a case report and review of the literature.

Journal of ovarian research·2016
Same author

A DNA tetrahedron-based molecular beacon for tumor-related mRNA detection in living cells.

Chemical communications (Cambridge, England)·2016
Same author

Long non-coding RNA MALAT-1 is downregulated in preeclampsia and regulates proliferation, apoptosis, migration and invasion of JEG-3 trophoblast cells.

International journal of clinical and experimental pathology·2016

相关实验视频

Updated: Jan 11, 2026

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

999

在远程传感图像中用于水平框监督定向对象检测的实例级定向增强.

Yang Xu, Zifang Xu, He Wang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |November 19, 2025
    PubMed
    概括

    本研究介绍了ILOEDet,这是一种用于使用水平界限框进行定向物体检测的新型探测器. 它通过学习特定实例的特征来增强导向灵敏度,提高遥感的准确性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 遥感 遥感 遥感 遥感
    • 机器学习 机器学习

    背景情况:

    • 遥感数据集主要使用水平界限框 (HBB),与需要面向界限框 (OBB) 的定向物体检测方法产生不匹配.
    • 现有的水平框监督定向对象检测方法在学习特定实例的定向特征方面面临限制,原因是图像级的转换和无效的特征提取.
    • 传统方法通常依赖于数据增强来提高方向意识,但未能有效地利用标准CNN提取方向敏感特征.

    研究的目的:

    • 为了解决水平框监督定向物体检测现有方法的局限性.
    • 提出一种新型探测器,即实例级定向信息增强探测器 (ILOEDet),以提高定向灵敏度.
    • 增强模型学习特定实例定向特征的能力,并将对象定向与整体图像上下文脱.

    主要方法:

    • 集成实例识别旋转卷积模块 (IARCM) 来嵌入实例级定向信息.
    • 实施一个实例级翻转一致性 (IFC) 机制,通过自我监督学习来实现强大的导向学习.
    • 利用IARCM内部的分类和中心度得分来选择高质量的实例并指导旋转卷积操作.

    主要成果:

    • 拟议的ILOEDet有效地提高了对象检测中的定位灵敏度.
    • 对DOTA,HRSC2016和DIOR-R数据集的实验证明了拟议方法的卓越性能.

    更多相关视频

    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

    9.5K
    Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
    08:16

    Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

    Published on: October 24, 2025

    448

    相关实验视频

    Last Updated: Jan 11, 2026

    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

    999
    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

    9.5K
    Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
    08:16

    Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

    Published on: October 24, 2025

    448
  • 实例意识旋转卷积模块和实例级翻转一致性机制有助于增强特征提取和定向学习.

    • 结论:

    • ILOEDet在水平框监督定向物体检测方面取得了重大进展.
    • 拟议的实例级学习机制克服了图像级转换和传统CNN的局限性.
    • 该方法为远程传感应用中的定向物体检测提供了更强大,更有效的解决方案.