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Digital Image Correlation or Particle Image Velocimetry?

Alexander Jacobson
Posted 11 years ago

Hey Wolfram Community,

I would like to use Mathematica's image processing functionality to visualize the difference between these two images. Here's what's going on here: I am heating the pins, and the hot air rising from the pins is distorting the randomized background. My goal is to visualize the air plumes resulting from heating the pins.

Thus far I have tried to use ImageFeatureTrack on the two images, which gives me a list of points, but I don't know how to turn these points into a meaningful visualization on top of the original image. My ideal visualization would be lines drawn from their position in the first image to their distorted position in the second image, perhaps colored as a function of their length (i.e. areas with more distortion would have a more intense color).

I've spent about two days watching tutorials and to learn the Wolfram Language, but I'm not able to figure out which functions to use, and I keep getting errors about data types passing from one function to the next. Even an explanation of the right strategy for achieving this would be really helpful. Attached is a notebook with the two images, and my attempts so far.

Thanks, Alexander

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POSTED BY: Alexander Jacobson
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Hey,

I just wanted to update the forum on progress. I was able to use the nuance of Photoshop to dial in the subtract features and achieve results like these.

For developing this technique, some alignment algorithms that don't deform the image could be really useful, but for me it's easier to do this manually in Photoshop than to achieve it in Mathematica.

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POSTED BY: Alexander Jacobson

Marco, thanks for such a thorough reply! It seems I was headed in the wrong direction and these images represent some great progress.

I put Mathematica down for a while and did some more research on the most algorithms to use for this problem. This article seems to demonstrate the most success, as there are several scales of phenomena visible, especially using Brox's algorithm. I have followed their instructions for capturing the images, so I think the weakness in my images is in the image-processing.
https://www.cs.ubc.ca/nest/imager/tr/2008/Atcheson_BOS_EiF/

Example Background-Oriented Schlieren Technique

The subtract function is good for making the phenomena visible, and I'm able to enhance it somewhat in Photoshop, but the underlying problem is that it is only identifying disturbance, rather than quantifying the amount of disturbance. The images are more intense where the air moves fastest, but they aren't nearly as clear as the images in this paper. Are any of the image functions capable of running more nuanced algorithms for tracking movement from one image to another?

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POSTED BY: Alexander Jacobson

By the way, this is what you get with ImageCorresponding points:

   Show[ImageResize[images[[1]], 300], 
 Graphics[{Green, Disk[#[[1]], 2]} & /@ 
   Transpose[
    ImageCorrespondingPoints[ImageResize[images[[1]], 300], 
     ImageResize[images[[2]], 300]]]], 
 Graphics[{Red, Disk[#[[2]], 2]} & /@ 
   Transpose[
    ImageCorrespondingPoints[ImageResize[images[[1]], 300], 
     ImageResize[images[[2]], 300]]]], 
 Graphics[{Thick, Line[#]} & /@ 
   Transpose[
    ImageCorrespondingPoints[ImageResize[images[[1]], 300], 
     ImageResize[images[[2]], 300]]]]]

which gives

enter image description here

so this does not really appear to be useful for you.

Cheers, Marco
POSTED BY: Marco Thiel
POSTED BY: Marco Thiel
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