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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

972
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.
972
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

131
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...
131
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

174
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...
174
Deconvolution01:20

Deconvolution

263
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
263
Distance Measurements by Taping01:18

Distance Measurements by Taping

108
Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
108
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

137
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
137

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

The Dual Roles of Regulatory B Cells in Infection, Cancer, and Immunity.

MedComm·2026
Same author

STAMP: Spatial-Temporal Anchored Motion Planning for Zero-Shot Continuous Vision-and-Language Navigation.

Sensors (Basel, Switzerland)·2026
Same author

The DNA-binding protein PfAP2-V regulates erythrocyte invasion and pathogenesis of the human malaria parasite Plasmodium falciparum.

Science China. Life sciences·2026
Same author

MAGIC: Meta-Ability Guided Interactive Chain-of-Distillation for Effective-and-Efficient Vision-and-Language Navigation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Immune-gut-metabolite Interactions in Multiple Sclerosis: A Mendelian Randomization and Mediation Study.

Current neurovascular research·2026
Same author

MAP-X unveils the shapeshifting interactome of Plasmodium falciparum.

Trends in parasitology·2026

相关实验视频

Updated: Sep 18, 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

650

DCPI-深度:在未经监督的单眼深度估计之前,明确注入密度对应.

Mengtan Zhang, Yi Feng, Qijun Chen

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |June 25, 2025
    PubMed
    概括

    本研究引入了DCPI-Depth用于无监督单眼深度估计. 它在具有挑战性的领域提高了准确性,例如使用新型几何约束的无纹和动态区域.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 无监督的单眼深度估计正在获得引力.
    • 挑战包括在缺乏纹理或动态区域精确的深度感知.

    研究的目的:

    • 通过结合密集的对应先验来增强无监督单眼深度估计.
    • 在现有框架中引入明确的几何约束.

    主要方法:

    • 使用三角深度图开发了一个上下文-几何深度一致性损失.
    • 引入了连接光流分歧和深度梯度的差异性质相关性损失.
    • 实施了对刚性和光学流的双向流同调整策略.

    主要成果:

    • 拟议的DCPI-Depth框架实现了最先进的性能.
    • 在多个公共数据集中表现出卓越的概括性.
    • 显示精确的深度估计在无纹理和动态区域,提高了光滑度.

    结论:

    • DCPI-Depth有效地解决了无监督单眼深度估计的局限性.
    • 新型组件提供了强大的几何约束,以提高精度.
    • 该框架为单眼视频的深度感知提供了显著的进步.

    更多相关视频

    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
    07:45

    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

    Published on: July 21, 2020

    4.6K
    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

    3.0K

    相关实验视频

    Last Updated: Sep 18, 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

    650
    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
    07:45

    Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

    Published on: July 21, 2020

    4.6K
    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

    3.0K