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

6.4K
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...
6.4K

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

Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.

BMC medical informatics and decision making·2026
Same author

Genotypic characterization of multidrug resistant Escherichia coli isolates reveals co-existence of ESBL- and carbapenemase- encoding genes linked to ISCR1.

Veterinaria italiana·2022
Same author

Fingolimod Plays Role in Attenuation of Myocardial Injury Related to Experimental Model of Cardiac Arrest and Extracorporeal Life Support Resuscitation.

International journal of molecular sciences·2019
Same author

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory.

Sensors (Basel, Switzerland)·2019
Same author

Cardioprotective Effects of Sphingosine-1-Phosphate Receptor Immunomodulator FTY720 in a Clinically Relevant Model of Cardioplegic Arrest and Cardiopulmonary Bypass.

Frontiers in pharmacology·2019
Same author

Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain.

International journal of medical informatics·2019
Same journal

Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.

Frontiers in artificial intelligence·2026
Same journal

DiscoVerse: multi-agent pharmaceutical co-scientist for traceable drug discovery and reverse translation.

Frontiers in artificial intelligence·2026
Same journal

EEG-based cognition-aware task classification and scheduling using enhanced fuzzy transition modeling.

Frontiers in artificial intelligence·2026
Same journal

Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.

Frontiers in artificial intelligence·2026
Same journal

Legal document summarization: a short review.

Frontiers in artificial intelligence·2026
Same journal

Generative AI adoption and its impact on teachers' self-efficacy and instructional confidence in Ghana.

Frontiers in artificial intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

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

529

对伪装物体检测的歧视性上下文感知网络.

Chidiebere Somadina Ike1, Nazeer Muhammad2, Nargis Bibi3

  • 1Department of Computing, Atlantic Technological University, Letterkenny, Ireland.

Frontiers in artificial intelligence
|April 11, 2024
PubMed
概括
此摘要是机器生成的。

动物使用伪装来保护,但检测伪装的物体是具有挑战性的. 我们的歧视性上下文感知网络 (DiCANet) 通过增强特征表示和改进预测以获得更好的准确性来改进伪装对象检测 (COD).

关键词:
这就是CODD.人工智能是一种人工智能.一个基准的基准指标.伪装对象检测 伪装对象检测卷积神经网络是一种卷积神经网络.数据集数据集数据集深度学习是一种深度学习.功能提取 特性提取

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

9.0K
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

140

相关实验视频

Last Updated: Jun 28, 2025

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

529
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
A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

140

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 动物利用伪装来生存,这给检测带来了挑战.
  • 伪装对象检测 (COD) 旨在识别与背景混合的对象.
  • 目前的COD方法因环境数据噪音而面临困难.

研究的目的:

  • 引入一个新的网络,歧视性上下文意识网络 (DiCANet),用于增强伪装对象检测 (COD).
  • 为了提高伪装物体检测的准确性和边界定义.

主要方法:

  • 这是一个两阶段的方法,包括一个自适应恢复块和一个级联检测模块.
  • 适应性恢复块优先考虑信息特征,以改善表示.
  • 级联检测模块使用扩大的受体场进行精细的预测,无需后处理.

主要成果:

  • 在基准COD数据集 (CAMO,CHAMELEON,COD10K) 上,DiCANet实现了最先进的性能.
  • 该方法产生准确的突出地图,详细的上下文和精确的对象边界.
  • 在不需要后处理步骤的情况下实现了性能.

结论:

  • 迪卡网有效地解决了在复杂环境中检测伪装物体的挑战.
  • 与现有方法相比,拟议的架构在COD任务中表现出优异的性能.
  • 在基准数据集上的实验验验证了DiCANet创新方法的有效性.