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

Nuclear Fusion02:45

Nuclear Fusion

33.7K
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
33.7K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

540
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
540
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.9K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.9K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

454
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
454
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

2.5K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
2.5K
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

8.4K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
8.4K

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

Scene-Dependent Prediction in Latent Space for Video Anomaly Detection and Anticipation.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Multi-view scene matching with relation aware feature perception.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

Learning to Compare Relation: Semantic Alignment for Few-Shot Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Genome sequence of the progenitor of wheat A subgenome Triticum urartu.

Nature·2018
Same author

Targeting CPT1A-mediated fatty acid oxidation sensitizes nasopharyngeal carcinoma to radiation therapy.

Theranostics·2018
Same journal

A boundary-regularization-enhanced video anomaly detection network based on context-adaptive spatio-temporal conditional diffusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

MT<sup>2</sup>-CSD and LLM-CRAN: A new dataset and an LLM-based multi-semantic knowledge fusion model for conversational stance detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TriAlignNet: A triple-path cross-modality alignment framework for multimodal time series forecasting.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jan 25, 2026

Procedure for the Development of Multi-depth Circular Cross-sectional Endothelialized Microchannels-on-a-chip
10:55

Procedure for the Development of Multi-depth Circular Cross-sectional Endothelialized Microchannels-on-a-chip

Published on: October 21, 2013

14.4K

多源时间深度融合为强大的端到端视觉测距.

Sihang Zhang1, Congqi Cao2, Qiang Gao3

  • 1School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an, PR China; School of Computer Science, Northwestern Polytechnical University, Xi'an, PR China.

Neural networks : the official journal of the International Neural Network Society
|January 23, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的端到端多源视觉测距 (MVO) 模型. 它通过整合时间数据和深度信息来增强姿势估计,提高视觉测距任务的准确性和效率.

关键词:
深度感知 感知深度感知单眼视觉测距仪使用单眼视觉测距仪.多来源的核聚变.位置估计 位置估计提取的提取方法时间序列时间序列

更多相关视频

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Multi-Photon Time Lapse Imaging to Visualize Development in Real-time: Visualization of Migrating Neural Crest Cells in Zebrafish Embryos
10:13

Multi-Photon Time Lapse Imaging to Visualize Development in Real-time: Visualization of Migrating Neural Crest Cells in Zebrafish Embryos

Published on: August 9, 2017

8.1K

相关实验视频

Last Updated: Jan 25, 2026

Procedure for the Development of Multi-depth Circular Cross-sectional Endothelialized Microchannels-on-a-chip
10:55

Procedure for the Development of Multi-depth Circular Cross-sectional Endothelialized Microchannels-on-a-chip

Published on: October 21, 2013

14.4K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Multi-Photon Time Lapse Imaging to Visualize Development in Real-time: Visualization of Migrating Neural Crest Cells in Zebrafish Embryos
10:13

Multi-Photon Time Lapse Imaging to Visualize Development in Real-time: Visualization of Migrating Neural Crest Cells in Zebrafish Embryos

Published on: August 9, 2017

8.1K

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 端到端视觉测距 (VO) 模型提供高定位精度并减少故障.
  • 当前的模型在使用全时间序列数据进行姿势优化时遇到困难.
  • 现有的方法不充分地使用深度预测来应对规模约束.

研究的目的:

  • 提出一个端到端的多源视觉测距 (MVO) 模型.
  • 将混合VO组件动态集成到一个统一的深度学习框架中.
  • 通过利用时间和深度信息来改善姿势估计.

主要方法:

  • 开发了TimePoseNet,以捕捉跨序列的时间依赖性,用于时间对位映射.
  • 采用波形卷积注意力机制来提取和嵌入全球深度信息.
  • 在姿势估计后处理阶段共同结合的时间和深度线索.

主要成果:

  • 在KITTI基准上取得了最先进的表现.
  • 在UAV-2025数据集上表现出最佳性能.
  • 在推理过程中保持计算效率.

结论:

  • 拟议的MVO模型有效地利用时间和深度数据进行增强的姿势估计.
  • 该框架提供了一种统一且可学习的视觉测距方法.
  • 该方法在视觉测距精度和效率方面取得了显著的进步.