Example: Scikit-image and trackpy (bubble tracking in foams)

[François Boulogne]

Le 20/11/2014 16:43, Emmanuelle Gouillart a écrit :
> Great, thanks! It seems that most processing pipelines rely on first
> detecting objects of interest (eg with a segmentation step) and then
> tracking such objects. Do you know of any (generic-enough) approach that
> would perform both steps at the same time, ie if you know that the same
> objects must be found in several images this information can be used for
> performing the segmentation?
>


That's a good question. I'm not aware of such algorithm.

It reminds me that machine learning could be eventually use also with
trackpy. I opened a PR showing how scikit-image and scikit learn could
be used together to detect digits:
https://github.com/scikit-image/skimage-demos/pull/3
A similar procedure could be used in the situation of several
populations of features with different shapes but similar sizes for
instance (like a mixture of colloidal spheres and cubes)

-- 
François Boulogne.
http://www.sciunto.org
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