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Development of Robust Correlation Algorithms for Image Velocimetry using Advanced Filtering

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dc.contributor.authorEckstein, Adricen
dc.contributor.committeechairVlachos, Pavlos P.en
dc.contributor.committeememberDuggleby, Andrew T.en
dc.contributor.committeememberPaul, Mark R.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T20:50:31Zen
dc.date.adate2008-01-18en
dc.date.available2014-03-14T20:50:31Zen
dc.date.issued2007-12-07en
dc.date.rdate2008-01-18en
dc.date.sdate2007-12-20en
dc.description.abstractDigital 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.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-12202007-004427en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12202007-004427/en
dc.identifier.urihttp://hdl.handle.net/10919/36338en
dc.publisherVirginia Techen
dc.relation.haspartETD_Eckstein_Adric_2.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectnonlinear least squares regressionen
dc.subjectphase correlationen
dc.subjectgeneralized cross correlationen
dc.subjecttime delay estimationen
dc.subjectDigital Particle Image Velocimetryen
dc.subjectDPIVen
dc.subjectimage processingen
dc.subjectdigital signal decompositionen
dc.subjectdigital filteringen
dc.titleDevelopment of Robust Correlation Algorithms for Image Velocimetry using Advanced Filteringen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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