Image correlation velocimetry

This paper focuses on the correlation of two successive scalar images for the purpose of measuring imaged fluid motions. A method is presented for deforming, or transforming, one image to another. Taylor series expansions of the Lagrangian displacement field are used, in conjunction with an integral form of the equations of motion, to approximate this transformation. The proposed method locally correlates images for displacements, rotations, deformations, and higher-order displacement gradient fields, and applies a global minimization procedure to insure a global consistency in the results. An integral form of the equations of motion is employed. No explicit spatial or temporal differentiation of the image data is required in estimating the displacement field. As a consequence, this method is appropriate for both continuous-scalar as well as discrete-particle-image data. Successive two-dimensional digital CCD images of fluid motion marked with dye, are used to verify the capabilities of the method. The utility of the method is also illustrated using a pair of Voyager 2 images of Jupiter.

[1]  Kenneth B. Southerland,et al.  A scalar imaging velocimetry technique for fully resolved four‐dimensional vector velocity field measurements in turbulent flows , 1992 .

[2]  Dimitris N. Metaxas,et al.  Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Ellen C. Hildreth,et al.  Measurement of Visual Motion , 1984 .

[4]  R. Adrian.  Particle-Imaging Techniques for Experimental Fluid Mechanics , 1991 .

[5]  David W. Murray,et al.  Experiments in the machine interpretation of visual motion , 1990 .

[6]  H. Schlichting.  Boundary Layer Theory , 1955 .

[7]  J. J. Wang,et al.  Limitation and improvement of PIV: Part I: Limitation of conventional techniques due to deformation of particle image patterns , 1993 .

[8]  S. Limaye.  Erratum - Jupiter - New Estimates of the Mean Zonal Flow at the Cloud Level , 1986 .

[9]  C. Koch,et al.  The analysis of visual motion: from computational theory to neuronal mechanisms. , 1986, Annual review of neuroscience.

[10]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[11]  R. Bindschadler,et al.  Satellite-Image-Derived Velocity Field of an Antarctic Ice Stream , 1991, Science.

[12]  Arne J. Pearlstein,et al.  On the determination of solenoidal or compressible velocity fields from measurements of passive or reactive scalars , 1995 .

[13]  Lambertus Hesselink,et al.  Auto-correlation measurements in three-dimensional fluid flow datasets , 1990 .

[14]  Brian J. Cantwell,et al.  Exact solution of a restricted Euler equation for the velocity gradient tensor , 1992 .

[15]  Werner J. A. Dahm,et al.  Direct, high resolution, four‐dimensional measurements of the fine scale structure of Sc≫1 molecular mixing in turbulent flows , 1991 .

[16]  Jin Luo,et al.  Computing motion using analog and binary resistive networks , 1988, Computer.

[17]  C. Willert,et al.  Digital particle image velocimetry , 1991 .

[18]  H. E. Fiedler,et al.  Limitation and improvement of PIV , 1993 .

[19]  M. Sutton,et al.  Estimation of stress intensity factor by digital image correlation , 1987 .

[20]  Richard B. Miles,et al.  Instantaneous profiles and turbulence statistics of supersonic free shear layers by Raman excitation plus laser-induced electronic fluorescence (Relief) velocity tagging of oxygen , 1989 .

[21]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .