PIVlab - Digital Particle Image Velocimetry Tool for MATLAB

PIVlab v2.30 features 'multipass window deformation ensemble correlation'. This type of correlation has been introduced by Santiago et al.  in 1998. It is especially helpful in micron-resolution particle image velocimetry (micro-PIV, µPIV), as it can deal with very low seeding densities (see below). Ensemble correlation PIV should be used if you want to analyze a steady flow that has a low seeding density. You need to record a large number of images to have the full benefit of ensemble correlation in PIVlab. The yellow lines show the interrogation areas (IAs). The white dots are particles, and you can see that there are hardly any particles in the IAs. The general rule of thumb for PIV is that there should be 5 - 15 particles per IA. This is clearly not the case here. Standard PIV algorithms will fail. In order to get a better result, you would have to increase the size of the IAs dramatically. With ensemble correlation, PIVlab analyzes a series of sparsely seed...

I am currently working on the implementation of ensemble correlation. This kind of correlation averages the correlation matrices of a frame series before searching for a peak. Using it makes sense, when there a only a few particles visible in the flow. This is often the case in micro-piv, and I hope that there will be many users that benefit from this feature. The changes to the GUI are finished, and I have a simple 1-pass ensemble correlation running. But I want to have the full potential of multi-pass window deformation algorithms with ensemble correlation. Implementation therefore takes a bit more time as there are some changes that challenge my brain. Additionally, most of the programming currently happens single handed because of my third daughter... Slow & cozy programming... 

New features / enhancements: New and enhanced correlation algorithm (linear correlation instead of circular correlation). Details here. Correlation quality dropdown menu: Normal: Circular correlation, linear window deformation Better: Circular correlation, spline window deformation High: Linear correlation, spline window deformation Extreme: Linear correlation, spline window deformation, repeated correlation Suggestion for selecting an appropriate interrogation area size. Works by trying to count the amount of texture / particles and calculating the maximum displacement. PIVlab will try to suggest settings so that every window contains sufficient texture / particles and the displacement is not too large. Quick acces toolbar for the most important PIVlab functions. Will not be displayed if there is not enough space in PIVlab's program window. Automatic histogram limiting and stretching works now seperately for both images of a frame. This accounts better for unevenly ill...

Recently, an interesting discussion , initiated by Theo, popped up in the PIVlab forum. Alex, the developer from OpenPIV also joined and gave valuable comments. The discussion was about whether circular correlation or linear correlation would be more suitable for processing PIV images (read the details in the link given above if you're interested). PIVlab uses circular correlation since the beginning. Now, I also tried linear correlation, and the results were pretty good: Linear correlation decreases bias and random (RMS) error, and it also enhances the robustness (less wrong vectors in bad image data). However, the processing time increases by a factor of 2.5 too. As there are too many settings in PIVlab (window deformation interpolator, repeated correlation, correlation type) that are difficult to understand for a standard user, I decided to remove these settings. In the latest release (coming in a few days), there is instead a drop-down box that looks like this: The user...