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

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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

Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data.

PloS one·2026
Same author

Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection.

Frontiers in artificial intelligence·2026
Same author

Enhanced generalized normal distribution optimizer with Gaussian distribution repair method and cauchy reverse learning for features selection.

Scientific reports·2026
Same author

The multi-level image segmentation in dermatology application using an enhance Secretary Bird Optimization Algorithm.

Scientific reports·2025
Same author

Memetic Salp Swarm Algorithm for economic load dispatch problems.

Scientific reports·2025
Same author

Deep learning-based feature selection for detection of autism spectrum disorder.

Frontiers in artificial intelligence·2025
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jun 30, 2026

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

3.7K

使用基于多策略Osprey优化算法的多级值进行空中图像细分.

Mohamed Abd Elaziz1,2, Mohammed Azmi Al-Betar3,4, Ahmed A Ewees5

  • 1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig , 14459, Egypt. abd_el_aziz_m@yahoo.com.

Scientific reports
|March 17, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种修改后的 Osprey 优化算法 (MOOA),用于空中图像分割. 通过优化多级值,MOOA提高了图像细分质量,提高了远程传感应用的准确性.

关键词:
空中图像细分的细分方法双重的吸引器是双重的吸引器.多策略机制多策略机制.多级值设置多级值设置Osprey 的优化算法 (OOA)

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

853
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

3.7K

相关实验视频

Last Updated: Jun 30, 2026

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

3.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

853
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

3.7K

科学领域:

  • 计算机视觉 计算机视觉
  • 遥感 遥感 遥感 遥感
  • 图像处理 图像处理

背景情况:

  • 空中图像细分对于各种应用,包括城市规划,环境监测和灾害管理至关重要.
  • 精确的细分依赖于有效的值技术,从空中图像中提取有意义的信息.
  • 现有的优化算法可能遭受过早的融合,限制了细分性能.

研究的目的:

  • 开发一个先进的多级值技术用于空中图像分割.
  • 通过使用多策略机制来提高Osprey优化算法 (OOA) 的性能.
  • 评估拟议的修改的 Osprey 优化算法 (MOOA) 在细分空中图像中的有效性.

主要方法:

  • 一个修改后的 Osprey 优化算法 (MOOA) 被开发出来,它包含了双重吸引器,用于增强的探索和动态随机搜索,用于改进的开发.
  • MOOA中的多策略机制旨在防止过早的融合,并提高整体绩效.
  • MOOA应用于航空图像分割的多级值,并使用16张航空图像进行验证.

主要成果:

  • MOOA在确定最佳值方面表现出很高的能力,显著提高了细分图像质量.
  • 性能指标,如峰值信号与噪声比率 (PSNR),特征相似性指数测量 (FSIM) 和结构相似性指数测量 (SSIM) 显示了显著的改善.
  • 与已建立的多级值方法进行比较分析证实了MOOA的优越性.

结论:

  • 拟议的MOOA有效地解决了标准OOA的局限性,在空中图像细分方面提供了卓越的性能.
  • 开发的技术为从航空图像中提取准确信息提供了强大的解决方案.
  • MOOA代表了计算机视觉和遥感任务的有希望的进步,这些任务需要精确的图像细分.