# Frequently Asked Questions about PIV parameters¶

1. Can you please elaborate on the `sclt`

parameter which is passed to the openpiv function.
E.g. if the time between the two consecutive image is 0.5 seconds and 1 pixel in the image corresponds to 50 cms, what would be the value of sclt.

`sclt`

is a shortcut for _scaling factor from displacement to velocity units_. It’s also called the _scale_, or _scaling_.

PIV provides the local displacement in pixel units. In order to know the displacement in the real physical units you multiply it by the scaling of **cm/pixel**, i.e. by 50 cm/pixel. To know the speed, the displacement is divided by the time separation, i.e. by 0.5 seconds, then we get:
scaling = sclt = 50 cm/pixels / 0.5 = 100 [cm/seconds/pixels]

For example, if the vector is 10 pixels, then the result will be 100 * 10 = 1000 cm/s

- Whats the purpose of the local and global filtering?

**global filtering** supposingly removes the obvious **outliers**, i.e. the vectors which length is larger than the mean of the flow field plus 3 times its standard deviation. These are global outliers in the statistical sense.

**local filtering** is performed on small neighborhoods of vectors, e.g. 3 x 3 or 5 x 5, in order to find **local outliers** - the vectors that are dissimilar from the close neighbors. Typically there are about 5 per-cent of erroneous vectors and these are removed and later the missing values are interpolated from the neighbor vector values. This is also a reason for the Matlab version to generate three lists of files:
**raw** - **_noflt.txt**
**filtered** (after global and local filters) - **_flt.txt**
final (after filtering and interpolation) - **.txt**

- Why, while taking the FFT, we use the Nfft parameter?

ffta=fft2(a2,Nfft,Nfft); fftb=fft2(b2,Nfft,Nfft);

and why the size has been specified as Nfft which is twice the interrogation window size.

In the FFT-based correlation analysis, we have to pad the window with zeros and get correlation map of the right size and avoid aliasing problem (see Raffel et al. 2007)

4. Also in the same function why sub image **b2** is rotated before taking the correlation.
b2 = b2(end:-1:1,end:-1:1);

Without rotation the result will be convolution, not correlation. The definition is **ifft(fft(a)*fft(conj(b)))**. conj() is replaced by rotation in the case of real values. It is more computationally efficient.

- In the find_displacement(c,s2nm) function for finding peak2, why neighbourhood pixels around peak1 are removed? %line no:352

These peaks might appear as ‘false second peak’, but they are the part of the same peak. Think about a top of a mountain. You want to remove not only the single point, but cut out the top part in order to search for the second peak.

6. In the read_pair _of_images( ) function why A = double(A(:,:,1))/255; %line no:259 B = double(B(:,:,1))/255;

In order to convert RGB to gray scale. Not always true.

- After the program is executed, the variable vel contains all the parameters for all the velocity vectors. Here what are the units of u & v. Is it in metres/second?

It is not, the result depends on the **SCLT** variable. if it SCLT is 1, then it is in **pixels/dt** (dt is the interval between two images).

- What is the “Outlier Filter Value” in OpenPIV?

The outlier filter value is the threshold of the global outlier filter and is says how many times the standard deviation of the whole vector field is exceeded before the vector is considered as outlier. See above discussion on the filters.

The fifth column is the value of the Signal-To-Noise (s2n) ration. Note that the value is different (numerically) if the user choses Peak-to-Second-Peak ratio as the s2n parameter or Peak-to-Mean ratio as s2n parameter. The value of Peak-to-Second-Peak or Peak-to-Mean ratio is stored for the further processing.