[Vx2,Vy2] = PPPIV(Vx1,Vy1) carries out robust post-processing of 2-D PIV velocity data. Vx1 and Vy1 must be two matrices of same size that contain the x- and y-components of the velocities at equally spaced points in the Cartesian plane.
PPPIV uses a robust penalized least squares method that makes the smoothed output (Vx2,Vy2) no dependent upon the outlying (spurious) vectors: outliers are replaced and velocity vectors are smoothed using a single automated process.
MISSING DATA: Non finite (NaN or Inf) values in (Vx1,Vy1) are considered as missing velocities. The algorithm replaces them automatically.
PPPIV uses SMOOTHN. See SMOOTHN for more details:
http://www.mathworks.com/matlabcentral/fileexchange/25634
[Vx2,Vy2] = PPPIV(Vx1,Vy1,ROI) post-processes (Vx1,Vy1) in the region of interest defined by the binary matrix ROI: 1 => inside the region of interest, 0 => outside (i.e. masked data).
By default, PPPIV selects the smoothing parameter automatically by minimizing the GCV score (see reference #1 for details). Alternatively, the amount of smoothing can be somewhat adjusted by adding one of the three smoothing options:
[...] = PPPIV(Vx1,Vy1,OPTION) or [...] = PPPIV(Vx1,Vy1,ROI,OPTION)
The available options are:
'2x2' - weak smoothing
'3x3' - medium smoothing
'nosmoothing' - extremely weak smoothing
PPPIV (no input/output argument) runs one example.
Enter 'help pppiv' for more details.
References
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1) Garcia D, Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 2010;54:1167-1178.
2) Garcia D, A fast all-in-one method for post-processing of PIV data. Exp in Fluids, 2010; accepted pending revision.
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I also strongly suggest the excellent toolbox "PIVlab" written by William Thielicke and Eize J. Stamhuis:
http://www.mathworks.com/matlabcentral/fileexchange/27659
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http://www.biomecardio.com
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