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Absolute Motion Analysis- General Plane Motion

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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.
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Relative Motion Analysis using Rotating Axes-Problem Solving

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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.
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Relative Motion Analysis - Velocity

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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.
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Related Experiment Video

Updated: May 23, 2026

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Motion-Acuity Test for Visual Field Acuity Measurement with Motion-Defined Shapes

Published on: February 23, 2024

Two-dimensional pattern motion analysis uses local features.

L S Delicato¹, I Serrano-Pedraza, M Suero

¹Department of Psychology, David Goldman Informatics Center, University of Sunderland, Sunderland, United Kingdom. Louise.Delicato@Sunderland.ac.uk

Vision Research

|April 10, 2012

Summary

This summary is machine-generated.

Local features, not just spatial frequencies, influence motion perception. Our study shows the visual system uses local pattern details, like contrast differences, to determine object movement direction.

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Area of Science:

Visual neuroscience
Perception science

Background:

The visual system typically computes pattern motion from oriented spatial frequency components.
Emerging evidence suggests local features also play a role in motion perception.

Purpose of the Study:

To demonstrate the contribution of local features to motion perception.
To investigate how the visual system integrates information from different pattern components.

Main Methods:

Creation of complex plaid stimuli with alternating high and low contrast rows.
Manipulation of stimuli so that spatial frequency components move coherently, but local features move divergently.
Direction discrimination task with observer reports on perceived motion.

Main Results:

Observers perceived opposite directions of motion in high-contrast versus low-contrast rows.
This perception contrasted with predictions based solely on spatial frequency components.
Results support the hypothesis that local features are utilized in pattern motion computation.

Conclusions:

The visual system incorporates local features when computing pattern motion.
A localized energy model with broadly tuned orientation sensors can explain these findings.
This highlights the complexity of motion perception beyond global component analysis.