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 Experiment Video

Updated: Jan 10, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

Learning Domain-Invariant Representations for Event-Based Motion Segmentation: An Unsupervised Domain Adaptation

Mohammed Jeryo1, Ahad Harati1

  • 1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Mashhad 9177948974, Iran.

Journal of Imaging
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

865
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...
865
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

685
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
685
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

510
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...
510

You might also read

Related Articles

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

Sort by
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

This study introduces a novel framework for motion segmentation using event cameras, successfully adapting knowledge from RGB data to event streams. The method achieves state-of-the-art results on challenging benchmarks, offering a lightweight and efficient solution for high-speed applications.

Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Event cameras offer high temporal resolution and dynamic range, ideal for high-speed applications.
  • Sparsity of event data and lack of annotations hinder supervised learning for motion segmentation.
  • Domain adaptation is difficult due to significant shifts between intensity images and event data.

Purpose of the Study:

  • To develop a cross-modality adaptation framework for motion segmentation from event streams.
  • To transfer knowledge from labeled RGB-flow data to unlabeled event data.
  • To address challenges of data sparsity, annotation scarcity, and domain shift in event-based vision.

Main Methods:

  • A dual-branch encoder extracts features from RGB and optical flow in the source domain.
Keywords:
cross-modality learningevent cameramotion segmentationreal-time inferenceunsupervised domain adaptation

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Related Experiment Videos

Last Updated: Jan 10, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000
  • Reconstruction networks convert event data into pseudo-image and pseudo-flow modalities for the target domain.
  • Multi-level consistency losses enforce domain alignment on features, predictions, and outputs.

    • Main Results:

    • The proposed framework achieves 83.1% accuracy on EVIMO2 and 79.4% on MOD++.
    • Outperforms existing methods like EV-Transfer and SHOT by up to 3.6%.
    • Demonstrates a lightweight architecture enabling real-time inference with enhanced mIoU and F1 Score.

    Conclusions:

    • The cross-modality adaptation framework effectively bridges the domain and modality gap for event-based motion segmentation.
    • The method enables acquisition of domain-invariant, semantically rich features with reduced training costs.
    • The approach is suitable for real-world high-speed applications like autonomous driving.