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

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

452
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
452
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

351
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
351
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

64
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...
64
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

218
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
218
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

328
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
328
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

60
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...
60

You might also read

Related Articles

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

Sort by
Same author

Impact-Resistant Hydrogels Via Quaternary Ammonium-Regulated Networks.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

The Role of 5-HT and DA Receptor Genes in Starvation-Induced Anxiety Behavior of <i>Portunus trituberculatus</i>.

Genes·2026
Same author

A collagen-functionalized biomimetic patch leveraging macromolecular piezoelectricity and anti-adhesive properties for abdominal wall defect repair.

International journal of biological macromolecules·2026
Same author

Wearable Ultrathin Self-Powered Sensing System Enabled by 3D Network CNTs-BP/MOF Film.

ACS sensors·2026
Same author

Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.

Nature communications·2026
Same author

Improved Cardiac Function and Glycemic Control in Elderly Diabetic Patients Through Structured Case Management After CABG.

Medical science monitor : international medical journal of experimental and clinical research·2026

Related Experiment Video

Updated: Jun 19, 2025

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.2K

Event-Based Optical Flow via Transforming Into Motion-Dependent View.

Zengyu Wan, Ganchao Tan, Yang Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 26, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Motion View-based Network (MV-Net) for enhanced event-based optical flow estimation. The network improves motion representation by transforming event data into a motion-dependent view, outperforming existing methods.

    More Related Videos

    Determining 3D Flow Fields via Multi-camera Light Field Imaging
    14:25

    Determining 3D Flow Fields via Multi-camera Light Field Imaging

    Published on: March 6, 2013

    16.6K
    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    661

    Related Experiment Videos

    Last Updated: Jun 19, 2025

    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
    13:02

    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

    Published on: February 27, 2016

    12.2K
    Determining 3D Flow Fields via Multi-camera Light Field Imaging
    14:25

    Determining 3D Flow Fields via Multi-camera Light Field Imaging

    Published on: March 6, 2013

    16.6K
    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    661

    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • Event cameras capture temporal dynamics, aiding optical flow estimation.
    • Challenges in event data processing include feature extraction and motion-appearance entanglement.

    Purpose of the Study:

    • To enhance event-based motion representation for optical flow estimation.
    • To introduce a novel Motion View-based Network (MV-Net) for practical applications.

    Main Methods:

    • Developed an Event View Transformation Module to create motion-dependent views.
    • Implemented a two-phase module: temporal clue extraction (central difference) and motion pattern perception (evolution-guided deformable convolution).
    • Utilized an eccentric downsampling process to mitigate event sparsity issues.

    Main Results:

    • The proposed MV-Net demonstrates superior performance on four challenging datasets.
    • Achieved state-of-the-art (SOTA) results in event-based optical flow estimation.
    • The network was trained end-to-end in a self-supervised manner.

    Conclusions:

    • The novel motion-dependent view transformation effectively enhances event-based motion representation.
    • MV-Net offers a practical and high-performing solution for optical flow estimation using event cameras.
    • Self-supervised end-to-end training enables efficient model development and deployment.