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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.5K
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.
1.5K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

724
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...
724
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

597
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...
597

You might also read

Related Articles

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

Sort by
Same author

Plasmonic Modulation of the Upconversion Luminescence Based on Gold Nanorods for Designing a New Strategy of Sensing MicroRNAs.

Analytical chemistry·2020
Same author

Vitamin D deficiency and metabolic syndrome in elderly Chinese individuals: evidence from CLHLS.

Nutrition & metabolism·2020
Same author

Author Correction: Genome-wide identification of and functional insights into the late embryogenesis abundant (LEA) gene family in bread wheat (Triticum aestivum).

Scientific reports·2020
Same author

Confirmation of the absence of local transmission and geographic assignment of imported falciparum malaria cases to China using microsatellite panel.

Malaria journal·2020
Same author

circMET promotes NSCLC cell proliferation, metastasis, and immune evasion by regulating the miR-145-5p/CXCL3 axis.

Aging·2020
Same author

Cyclic Peptide Extracts Derived From <i>Pseudostellaria heterophylla</i> Ameliorates COPD <i>via</i> Regulation of the TLR4/MyD88 Pathway Proteins.

Frontiers in pharmacology·2020

Related Experiment Video

Updated: Dec 6, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.0K

First- And Third-Person Video Co-Analysis By Learning Spatial-Temporal Joint Attention.

Huangyue Yu, Minjie Cai, Yunfei Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 12, 2020
    PubMed
    Summary

    This study introduces a novel method for analyzing first-person and third-person videos together. The approach uses "joint attention" to learn shared information across different video views, improving cross-view video analysis.

    More Related Videos

    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

    9.4K
    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
    07:09

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

    Published on: November 14, 2018

    11.3K

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    8.0K
    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

    9.4K
    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
    07:09

    Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

    Published on: November 14, 2018

    11.3K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Wearable devices generate increasing numbers of first-person videos, offering unique perspectives.
    • Analyzing videos from disparate viewpoints (first-person vs. third-person) presents significant challenges.
    • Co-analyzing cross-view videos is crucial for extracting comprehensive information.

    Purpose of the Study:

    • To develop a novel, learning-based method for cross-view video co-analysis.
    • To leverage the concept of

    Main Methods:

    • A multi-branch deep network is proposed to extract cross-view joint attention and shared representations.
    • Self-supervised learning with spatial constraints is employed for simultaneous feature extraction.
    • A temporal transition model is integrated to capture spatial-temporal joint attention.

    Main Results:

    • The proposed method achieves state-of-the-art performance on standard cross-view video matching tasks.
    • Experimental results validate the effectiveness of learning joint information across views.
    • The method demonstrates robustness in capturing essential temporal information.

    Conclusions:

    • The developed joint attention mechanism effectively links and analyzes information from different video perspectives.
    • This approach offers a robust solution for cross-view video co-analysis.
    • The learned joint representations have broad applicability in various video analysis tasks.