Fringe counting by machine learning algorithm!

We have mentioned previously that we are hoping to use volunteer classifications to train a machine learning algorithm to help us do further (and quicker!) analysis on the high speed electronic speckle pattern interferometry images like the ones we are looking at in this project.

Our collaborator at Belmont University in Tennessee has made quite a bit of progress using the averages of all the classifications done so far. Here is a short clip of some of his results that show how his algorithm has identified antinodes and counted the fringes of each of them:

Animation of algorithm identifying antinodes on steelpan imaged by ESPI

This doesn’t mean we don’t need you to keep working on our project!!  It is critical that we keep going in order to validate the algorithm and, if possible, make it better.

Thanks for your interest in the project – we will have more updates soon!


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