Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

61
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...
61
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

67
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...
67
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

627
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.
627
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

32
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
32
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

4.9K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
4.9K
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

43
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...
43

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bioorthogonal release-mediated targeted degradation of tripartite motif containing 24 protein for atherosclerosis therapy.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

A Geometric Framework for Absolute Pose and Velocity Estimation With Event Cameras.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Prompt mechanisms in medical imaging: A comprehensive survey.

Innovation (Cambridge (Mass.))·2026
Same author

Tectochrysin Ameliorates Colitis-Associated Colon Cancer in Mice Via the Activation of Mitophagy in Intestinal Epithelial Cells.

Molecular nutrition & food research·2026
Same author

Synergistic event-SVE imaging for quantitative propellant combustion diagnostics.

Optics express·2026
Same author

A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment.

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

Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

Optics letters·2026
Same journal

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

Optics letters·2026
Same journal

Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

Optics letters·2026
Same journal

Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

Optics letters·2026
Same journal

Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

Optics letters·2026
Same journal

Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

Optics letters·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

Event-based depth estimation with dense occlusion.

Kangrui Zhou, Taihang Lei, Banglei Guan

    Optics Letters
    |June 14, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new event camera method for accurate depth estimation behind occlusions. The novel approach achieves precise depth measurements with minimal relative errors, improving computer vision applications.

    More Related Videos

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    15.7K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    1.6K

    Related Experiment Videos

    Last Updated: Jun 23, 2025

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.5K
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    15.7K
    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
    09:41

    Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    Published on: April 21, 2023

    1.6K

    Area of Science:

    • Computer Vision
    • Robotics
    • Sensor Fusion

    Background:

    • Occlusions present a significant challenge in depth estimation for critical applications like autonomous driving and surveillance.
    • Existing methods struggle to accurately determine depth in scenes with dense occlusions.

    Purpose of the Study:

    • To develop a novel depth estimation method capable of accurately measuring depth behind occlusions using event cameras.
    • To address the limitations of current depth estimation techniques in occluded environments.

    Main Methods:

    • A two-step procedure involving rough and precise estimation using event camera data.
    • Rough estimation reconstructs event streams to remove occlusions and employs binocular intersection for initial depth calculation.
    • Precise estimation utilizes a novel criterion based on maximum edge length in reconstructed images to refine depth accuracy.

    Main Results:

    • The proposed method successfully estimates depth behind dense occlusions.
    • Experimental validation shows relative depth estimation errors below 1.05%.
    • The method demonstrates high accuracy and robustness in challenging occluded scenarios.

    Conclusions:

    • The developed event camera-based method offers a significant advancement in depth estimation for occluded scenes.
    • This technique has strong potential for enhancing perception systems in autonomous driving, remote sensing, and video surveillance.
    • The precise estimation criterion provides a reliable approach for recovering depth information obscured by objects.