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Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

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.
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Related Experiment Video

Updated: Jul 7, 2026

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Simultaneous motion estimation and filtering of image sequences.

C M Fan¹, N M Namazi

¹U.S. Patent and Trademark Office, Crystal City, VA 22202, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 13, 2008

Summary

This summary is machine-generated.

This study introduces a novel algorithm for simultaneous motion estimation and image sequence filtering. It effectively estimates motion and filters noise in images using maximum-likelihood and LMMSE principles.

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

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Last Updated: Jul 7, 2026

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

Digital Image Processing
Computer Vision
Signal Processing

Background:

Image sequences often suffer from noise and motion blur.
Accurate motion estimation is crucial for various image processing tasks.
Existing methods may struggle with simultaneous motion estimation and filtering.

Purpose of the Study:

To develop an algorithm for simultaneous estimation of multi-frame motion and filtering of image sequences.
To address the challenge of additive white Gaussian noise (AWGN) in image sequences.
To integrate motion estimation and image filtering into a unified framework.

Main Methods:

Utilizes the maximum-likelihood (ML) principle for estimating relative motion (dk(x)) between frames.
Employs the linear minimum mean square error (LMMSE) criterion for filtering the reference frame.
Combines motion estimation and frame filtering within a single algorithmic process.

Main Results:

The proposed algorithm simultaneously estimates motion and filters image sequences.
Demonstrates effective handling of additive white Gaussian noise (AWGN).
Simulation experiments using an affine motion model validate the method's performance.

Conclusions:

The presented algorithm offers a robust solution for joint motion estimation and image sequence filtering.
The integration of ML and LMMSE principles provides improved performance in noisy conditions.
The method shows promise for applications requiring accurate motion analysis and clean image sequences.