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

Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

1.4K
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
1.4K
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.5K
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
1.5K
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
Structural Classification of Joints01:20

Structural Classification of Joints

8.8K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
8.8K
Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

856
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
856

You might also read

Related Articles

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

Sort by
Same author

Clinical characteristics and antibody responses to Omicron variants among pregnant women in China during the December 2022-April 2023 COVID-19 pandemic wave.

Frontiers in immunology·2026
Same author

Safety, immunogenicity, and long COVID outcomes following inactivated COVID-19 vaccine boosters in elderly Chinese: a prospective cohort study.

Frontiers in immunology·2026
Same author

Indel pattern-guided repair mapping reveals genome-wide DNA repair networks in CRISPR/Cas9 editing.

Nucleic acids research·2026
Same author

Age-Period-Cohort analysis of gonorrhea surveillance data in Eastern China from 2005 to 2024.

BMC infectious diseases·2026
Same author

Menstrual and reproductive factors and risk of Alzheimer's disease in elderly women: a cohort study in Eastern China.

Scientific reports·2026
Same author

An integrated surveillance in Zhejiang Province: ecological and pathogen survey of vectors and reservoir hosts in 2024.

Frontiers in veterinary science·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

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

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

1.5K

Nonrigid Structure From Motion via Sparse Representation.

Kun Li, Jingyu Yang, Jianmin Jiang

    IEEE Transactions on Cybernetics
    |July 18, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel sparse representation method for nonrigid structure from motion, effectively handling occlusions using matrix completion. The approach improves 3D shape and motion estimation accuracy, even with significant data loss.

    More Related Videos

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
    14:14

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

    Published on: April 16, 2017

    12.1K

    Related Experiment Videos

    Last Updated: Apr 6, 2026

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

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.5K
    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
    14:14

    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

    Published on: April 16, 2017

    12.1K

    Area of Science:

    • Computer Vision
    • 3D Reconstruction
    • Robotics

    Background:

    • Nonrigid structure from motion (NRSfM) is crucial for understanding dynamic scenes.
    • Occlusion presents a significant challenge in NRSfM, leading to incomplete data.
    • Existing methods struggle with accuracy when data is missing or noisy.

    Purpose of the Study:

    • To develop a robust NRSfM method capable of handling occlusions.
    • To leverage sparse representation and matrix completion for improved 3D reconstruction.
    • To enhance the accuracy of estimating 3D shapes and motions in dynamic environments.

    Main Methods:

    • Utilizing sparse representation and matrix completion to address data loss due to occlusion.
    • Applying sparse transform for joint estimation of 3D shapes and motions.
    • Employing wavelet basis fitting for modeling complex 3D shape trajectories.

    Main Results:

    • Demonstrated superior performance in 3D shape and motion estimation compared to state-of-the-art methods.
    • Successfully recovered accurate 3D structures and motions from datasets with high occlusion percentages.
    • Showcased robustness against noise and outliers in observed data.

    Conclusions:

    • The proposed sparse representation approach offers a significant advancement in NRSfM with occlusion.
    • Matrix completion effectively reconstructs missing data, improving overall estimation accuracy.
    • Wavelet-based trajectory modeling enhances the handling of complex nonrigid motions.