all the pattern-recognition related posts here are dealing with face-, hand-, characters-recognition.
I wonder, is someone has successivly used OpenCV for recognizing cells on a picture from a microscope.
What I am currently able to do, is counting cells using threshold and shape-detection (change threshold, count shapes, discard shapes with invalide size).
My next task is recognizing among about 20 types of cells. It would be very interesting to exchange the experience. May be, the OpenCV is not the proper/overweighted tool for this?
Regards,
Valentin Heinitz
EDIT
An up-vote has drown my attention to this old question again. I completed the task finally with OpenCV. It works good and the tool was approved by FDA last year as a software part of a diagnostic device 🙂
Now I think OpenCV was the perfect tool for this, despite I had to implement Haralick-Features myself.
your problem is a machine learning problem. OpenCV offer a few tools for that, SVM would be a good fit for what you are trying to do. I have experience with Kohonen neural networks, which would be a good idea too, if you have a good database of your cells. However, I do not know how good you are in C++, but it is always a good idea to start with matlab, get your algorithm to work, and then rewrite it in C++. To test your ideas quickly, matlab is definitely a better tool, with a lot of ML functions availables.