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

2.6K
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.
2.6K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

1.1K
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...
1.1K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

844
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...
844

You might also read

Related Articles

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

Sort by
Same author

Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Integration of allocentric and egocentric visual information in a convolutional/multilayer perceptron network model of goal-directed gaze shifts.

Cerebral cortex communications·2022
Same author

Long-Range Augmented Reality with Dynamic Occlusion Rendering.

IEEE transactions on visualization and computer graphics·2021
Same author

Reducing bedtime physiological arousal levels using immersive audio-visual respiratory bio-feedback: a pilot study in women with insomnia symptoms.

Journal of behavioral medicine·2019
Same author

Fast and accurate vision-based stereo reconstruction and motion estimation for image-guided liver surgery.

Healthcare technology letters·2018
Same author

Dynamic Scene Recognition with Complementary Spatiotemporal Features.

IEEE transactions on pattern analysis and machine intelligence·2016

Related Experiment Video

Updated: Apr 4, 2026

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

5.1K

Spacetime Stereo and 3D Flow via Binocular Spatiotemporal Orientation Analysis.

Mikhail Sizintsev, Richard P Wildes

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study introduces a new method for 3D scene reconstruction using stereo vision. It accurately estimates structure and motion, even with complex surfaces, improving 3D computer vision.

    More Related Videos

    Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
    09:39

    Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

    Published on: November 18, 2019

    6.4K
    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

    17.3K

    Related Experiment Videos

    Last Updated: Apr 4, 2026

    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

    5.1K
    Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
    09:39

    Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

    Published on: November 18, 2019

    6.4K
    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

    17.3K

    Area of Science:

    • Computer Vision
    • 3D Reconstruction
    • Robotics

    Background:

    • Estimating 3D structure and motion from stereo images is crucial for dynamic scene understanding.
    • Traditional methods struggle with ambiguities and complex surfaces like transparent or specular materials.
    • Temporal coherence and integrating spatial-temporal information are key challenges in stereo vision.

    Purpose of the Study:

    • To develop a novel approach for recovering 3D structure and motion from binocular stereo image sequences.
    • To enhance disparity estimation by integrating spatial and temporal information for improved accuracy and temporal coherence.
    • To enable the recovery of multilayer disparity and dense 3D scene flow, even for challenging surfaces.

    Main Methods:

    • Matching spatiotemporal orientation distributions between left and right temporal image streams.
    • Utilizing a unified representation of local spatial and temporal structure for disparity estimation.
    • Implementing the approach on commodity GPUs using OpenCL for real-time performance.

    Main Results:

    • Achieved temporally coherent disparity estimates by combining spatial and temporal cues.
    • Successfully recovered multilayer disparity, resolving ambiguities present in single-source analysis.
    • Generated dense, robust 3D scene flow estimates, outperforming existing methods quantitatively and qualitatively.
    • Demonstrated accurate multilayer estimation for (semi)transparent and specular surfaces.

    Conclusions:

    • The proposed spatiotemporal orientation distribution matching offers a robust and accurate method for 3D dynamic scene recovery.
    • This approach significantly advances stereo vision capabilities, particularly for complex and dynamic environments.
    • Real-time performance achieved on GPUs makes this method practical for various applications.