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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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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...
854
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Absolute Motion Analysis- General Plane Motion01:24

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.
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...
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Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

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A slider-crank mechanism 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. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Updated: Jan 7, 2026

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Movement Artifact Direction Estimation Based on Signal Processing Analysis of Single-Frame Images.

Woottichai Nonsakhoo1, Saiyan Saiyod1

  • 1Hardware-Human Interface and Communications Laboratory (H2I-Comm Lab), Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study introduces the Movement Artifact Direction Estimation (MADE) algorithm for analyzing single-frame images. MADE accurately estimates movement artifact direction and magnitude, crucial for noise detection in image analysis.

Keywords:
direction detectiondirection estimationimpulse responsemotion blurmovement artifactmultiplicative noisenoise assessmentnoise detectionself-similarity analysissignal processingsingle-frame image

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Area of Science:

  • Image Processing
  • Signal Processing
  • Computational Imaging

Background:

  • Movement artifacts are critical noise sources in single-frame images.
  • Assessing artifact direction and magnitude is vital for accurate image analysis.
  • Existing methods face computational challenges in medical image quality assessment.

Purpose of the Study:

  • Introduce the Movement Artifact Direction Estimation (MADE) algorithm.
  • Enable accurate estimation of movement artifact direction and magnitude in single-frame images.
  • Address computational efficiency for real-time image quality assessment.

Main Methods:

  • Developed a signal processing-based algorithm (MADE) using 3D geometric analysis.
  • Utilized multi-directional quantification outputs (MAPE, ROPE, MAQ) from a preprocessing pipeline.
  • Conducted experiments under controlled optical camera imaging conditions with precision apparatus.

Main Results:

  • Demonstrated robust estimation of movement artifact direction (degrees) and magnitude (pixels).
  • Achieved close alignment between estimated parameters and ground truth.
  • Validated performance across various image shapes and velocities.

Conclusions:

  • The MADE algorithm provides a methodological proof of concept for movement artifact analysis.
  • Offers accurate directional and quantitative assessment of artifacts in single-frame images.
  • Highlights potential for efficient, instantaneous image quality assessment systems.