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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

Estimation of multiple, time-varying motions using time-frequency representations and moving-objects segmentation.

Dimitrios S Alexiadis¹, George D Sergiadis

¹Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece. dalexiad@mri.ee.auth.gr

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

|May 17, 2008

Summary

This study introduces a novel method for estimating multiple, time-varying object motions using signal processing techniques. The approach effectively identifies moving objects by analyzing their instantaneous frequencies and velocities.

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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

Area of Science:

Computer Vision
Signal Processing
Motion Estimation

Background:

Existing spatiotemporal methods struggle with estimating simultaneous, time-varying object motions.
Accurate tracking of multiple moving objects is crucial in various applications.

Purpose of the Study:

To develop an effective methodology for estimating time-varying motions of multiple objects.
To enable the identification of individual moving objects within a complex scene.

Main Methods:

The study equates multiple time-varying motion estimation to instantaneous frequency estimation of superimposed FM sinusoids.
Established signal processing tools, including time-frequency representations and fuzzy C-planes, are utilized.
The methodology involves analyzing energy concentration in 3-D space (spatial frequencies-instantaneous frequency) to estimate velocities.

Main Results:

The research demonstrates that energy concentrates along specific planes in the 3-D space for each time instant.
Instantaneous velocities are indirectly estimated using fuzzy C-planes.
The adapted approach successfully identifies individual moving objects.

Conclusions:

The proposed methodology effectively handles the estimation of multiple, time-varying object motions.
Experimental results validate the practical effectiveness of the developed approach for object identification and motion analysis.