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

Masking and Demasking Agents01:19

Masking and Demasking Agents

3.4K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.4K
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
Visual Agnosia01:12

Visual Agnosia

872
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
872
Reducing Line Loss01:18

Reducing Line Loss

337
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
337
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.1K
Aggregates Classification01:29

Aggregates Classification

941
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
941

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

Tonoplast sugar transporter StTST1 mediates vacuolar sugar partitioning and abiotic stress tolerance in potato.

Plant physiology and biochemistry : PPB·2025
Same author

Physical Properties of Mold Flux and Mineralogical Characteristics of Flux Film for Low-alloy Peritectic Steel Continuous Casting.

Materials (Basel, Switzerland)·2025
Same author

Regulation of osteogenic differentiation in vascular smooth muscle cells under high-glucose condition.

Frontiers in endocrinology·2025
Same author

Accurate structure prediction of cyclic peptides containing unnatural amino acids using HighFold3.

Briefings in bioinformatics·2025
Same author

Silibinin accelerates diabetic wound healing through PI3K/Akt-mediated immunomodulation-angiogenesis crosstalk.

Biochemical and biophysical research communications·2025
Same author

YTHDC2 inhibits HPV-positive cervical cancer growth by suppressing SLC7A11 in a ferroptosis-dependent manner.

Oncology letters·2025

相关实验视频

Updated: Jan 6, 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

3.3K

一个有条件细分引导的网络,用于在闭塞下完成石榴图像.

Duokuo Zhang1,2, Ruizhe Hou3, Jingjing Guo4

  • 1School of Information Engineering, Henan Institute of Science and Technology, Hongqi, Xinxiang, 453003, Henan, China. zhangduokuo@stu.hist.edu.cn.

Plant methods
|November 27, 2025
PubMed
概括

本研究介绍了有条件细分指导的扩散网络 (CSD-Net),以改善农业图像中的石榴果实检测. CSD-Net有效地重建了封闭的水果结构,提高了自动收获和产量估计的准确性.

关键词:
农业计算机视觉 农业计算机视觉条件扩散模型是一种条件扩散模型.图像细分 图像细分 图像细分多个尺度的条件聚变.石榴图像完整化 石榴图像完整化

更多相关视频

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

987
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

719

相关实验视频

Last Updated: Jan 6, 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

3.3K
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

987
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

719

科学领域:

  • 计算机视觉 计算机视觉
  • 农业技术 农业技术
  • 机器学习 机器学习

背景情况:

  • 在农业图像中,叶子和树枝的遮蔽阻碍了准确的石榴产量估计和自动收获.
  • 现有的图像完成方法在封闭的农业图像中扎着结构忠诚度.

研究的目的:

  • 开发一种新的高保真图像完成和封闭石榴果实细分的新框架.
  • 解决传统方法在恢复农业图像中的结构完整性的局限性.

主要方法:

  • 提出了有条件细分引导的扩散网络 (CSD-Net),一种轻量级的统一条件扩散模型.
  • 使用共享的编码器,分段分支和RGB扩散分支.
  • 杆分段面具作为结构先验,以指导扩散生成过程,以准确重建.

主要成果:

  • 与传统方法相比,CSD-Net实现了更高的性能,PSNR为30.37dB,SSIM为0.9490.
  • 该模型展示了水果结构的高保真重建,具有空间和纹理一致性.
  • 在117 MB的模型大小下,实现了高完成质量和推断效率之间的平衡.

结论:

  • CSD-Net提供了一种新且有效的解决方案,以减轻农业视觉感知中的封闭问题.
  • 拟议的有条件指导机制显著改善了封闭的石榴图像中的结构完整性恢复.
  • 这项工作通过在具有挑战性的农业条件下增强视觉感知来推进自动收获和产量估计.