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

Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

998
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...
998
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

805
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...
805
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

462
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...
462
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

629
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...
629
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

645
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...
645
Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

724
When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
724

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine.

Internal and emergency medicine·2025
Same author

A systematic literature review of spatio-temporal graph neural network models for time series forecasting and classification.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Interoperable Traceability in Agrifood Supply Chains: Enhancing Transport Systems Through IoT Sensor Data, Blockchain, and DataSpace.

Sensors (Basel, Switzerland)·2025
Same author

State-space modeling in long sequence processing: a survey on recurrence in the transformer era.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Continual learning of conjugated visual representations through higher-order motion flows.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

YOLO-S: A Lightweight and Accurate YOLO-like Network for Small Target Selection in Aerial Imagery.

Sensors (Basel, Switzerland)·2023
Same journal

Observer-based ADP for secure resource allocation in high-order nonlinear multi-agent systems under FDI attacks.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Concept mask-aware pruning and augmentation for few sample model compression.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Hindsight-based state space exploration via counterfactual intrinsic reward assignment.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Integrating visual and language cues via state space models for medical image segmentation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DNA: Improving text-based person search through distillation learning, negated relation-aware learning, and augmented representation learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

MCFusion-DDI: Multimodal cross-attention fusion of local-global features and latent drug associations for explainable DDI prediction.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Dec 24, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

1.0K

Learning visual features under motion invariance.

Alessandro Betti1, Marco Gori1, Stefano Melacci1

  • 1Department of Information Engineering and Mathematics, University of Siena, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|April 12, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new unsupervised learning theory for computer vision, leveraging motion invariance principles from visual data streams. This approach extracts features in multi-layer architectures, offering a biological inspiration for artificial intelligence.

Keywords:
Convolutional networksInformation-based learningInvariance of visual featuresNeural differential equationsPrinciple of least cognitive action

More Related Videos

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
07:24

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

387
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.5K

Related Experiment Videos

Last Updated: Dec 24, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

1.0K
Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
07:24

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

387
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.5K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Computational Neuroscience

Background:

  • Current computer vision algorithms primarily process static images, neglecting temporal dynamics and motion information.
  • This oversight limits their ability to interpret the natural, time-varying visual data humans perceive.
  • Existing methods often require extensive supervised learning, hindering efficient video signal processing.

Purpose of the Study:

  • To propose a novel theory of learning based on the principle of motion invariance for processing visual streams.
  • To develop a principled computational framework for discovering convolutional filters inspired by biological vision.
  • To enable unsupervised learning of features from video signals using a multi-layer architecture.

Main Methods:

  • Formulating a motion invariance principle derived from the temporal structure of visual data.
  • Developing a learning theory grounded in variational principles, analogous to physics.
  • Designing a multi-layer architecture for unsupervised feature extraction with motion invariance.

Main Results:

  • A new theory for unsupervised learning in computer vision, processing video signals effectively.
  • A computational scheme for discovering convolutional filters applicable to retinal processing.
  • Demonstration of motion-invariant feature extraction in a multi-layer system.

Conclusions:

  • The proposed motion invariance principle offers a principled approach to unsupervised video processing.
  • This theory provides a foundation for novel computer vision systems and sheds light on biological visual processing.
  • The approach reduces reliance on massive supervision, unlike traditional convolutional networks.