Title page for ETD etd-12202007-004427


Type of Document Master's Thesis
Author Eckstein, Adric
Author's Email Address Aeckstei@vt.edu
URN etd-12202007-004427
Title Development of Robust Correlation Algorithms for Image Velocimetry using Advanced Filtering
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Vlachos, Pavlos P. Committee Chair
Duggleby, Andrew T. Committee Member
Paul, Mark R. Committee Member
Keywords
  • nonlinear least squares regression
  • phase correlation
  • generalized cross correlation
  • time delay estimation
  • Digital Particle Image Velocimetry
  • DPIV
  • image processing
  • digital signal decomposition
  • digital filtering
Date of Defense 2007-12-07
Availability unrestricted
Abstract
Digital Particle Image Velocimetry (DPIV) is a planar measurement technique to measure the velocity within a fluid by correlating the motion of flow tracers over a sequence of images recorded with a camera-laser system. Sophisticated digital processing algorithms are required to provide a high enough accuracy for quantitative DPIV results. This study explores the potential of a variety of cross-correlation filters to improve the accuracy and robustness of the DPIV estimation. These techniques incorporate the use of the Phase Transform (PHAT) Generalized Cross Correlation (GCC) filter applied to the image cross-correlation. The use of spatial windowing is subsequently examined and shown to be ideally suited for the use of phase correlation estimators, due to their invariance to the loss of correlation effects.

The Robust Phase Correlation (RPC) estimator is introduced, with the coupled use of the phase correlation and spatial windowing. The RPC estimator additionally incorporates the use of a spectral filter designed from an analytical decomposition of the DPIV Signal-to-Noise Ratio (SNR). This estimator is validated in a variety of artificial image simulations, the JPIV standard image project, and experimental images, which indicate reductions in error on the order of 50% when correlating low SNR images. Two variations of the RPC estimator are also introduced, the Gaussian Transformed Phase Correlation (GTPC): designed to optimize the subpixel interpolation, and the Spectral Phase Correlation (SPC): estimates the image shift directly from the phase content of the correlation. While these estimators are designed for DPIV, the methodology described here provides a universal framework for digital signal correlation analysis, which could be extended to a variety of other systems.

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