Genetic algorithms are good at minimizing difficult, non-convex spaces so I imagine the foundations for such would be useful for image segmentation via expectation minimization as well. Josh On Tuesday, June 21, 2016 at 5:04:49 PM UTC-5, Juan Nunez-Iglesias wrote:

Hi Hakim!

I haven't seen something like what you're mentioning but I'm intrigued! If you can point to papers etc that implement something like this, we might be interested in including it in scikit-image. However, we only accept established algorithms, not the latest and greatest thing.

Juan.

On Sun, May 29, 2016 at 6:46 PM, Hakim Benoudjit <h.benoudjit@gmail.com> wrote:

...

Hello,

Is there a pixel-based region growing algorithm implemented with Scikit-image that would be employed for feature extraction on an image? Basically, the kind of algorithms I'm looking for (or just checking if someone came across it, to see if I'm in the right direction) works by adding pixels to the detected feature based on the minimization of a certain metric. Potentially, a pixel can be removed if the metric is not optimized when this pixel is added.

I'm thinking of implementing this feature extraction algorithm using Genetic Algorithms (GA), but I can't think of a way to make it possible to backtrack (i.e. remove a pixel). Anyone encountered something similar even if it's not using GAs?

-- You received this message because you are subscribed to the Google Groups "scikit-image" group. To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe@googlegroups.com. To post to this group, send email to scikit-image@googlegroups.com. To view this discussion on the web, visit https://groups.google.com/d/msgid/scikit-image/b151d21c-cf0b-4892-8910-f89f0... <https://groups.google.com/d/msgid/scikit-image/b151d21c-cf0b-4892-8910-f89f0c772064%40googlegroups.com?utm_medium=email&utm_source=footer> . For more options, visit https://groups.google.com/d/optout.