Dear Mohammad,

detecting and counting should be possible by image processing. Image segmentation by watershed (or any other segmentation approach) should work. In most image processing books are often presenting the segmentation of blood cells as example [1]. Your example image looks quite similar.

[1] http://biologicalproceduresonline.biomedcentral.com/articles/10.1007/s12575-009-9011-2

So thanks dear Daniel

Dear Mohammad,

I am not familiar with this systems. Sorry

YGA

I think the best way is to train a classifier to detect the stomata.

Crop from several images the example of stomata and areas with out stomata.

Use some image descriptor like Histogram of Gradients (Hog) to describe each one the patches.

Train a classifier, SVM for example, to distinguish between stomata and non stomata.

Use a sliding window and the classifier to detect the areas with the stomata.

For now if you don't have enough images  you can use the following code as a staring point. It uses one of the stomata in the image as an example in order to do correlation base detection.

Pay attention that the right low stomata is not detected because it is not a full stomata. You can change the patch to be just the upper side and also detect the right low stomata but then you would have to change some basic staff in the final part of the code.

Hope it is a good base for you good luck.

So thanks every body for attention and precious help. God bless you all.

Hi Mohammad,

How did you go with this project?

I am looking at doing a similar procedure.

Have you ever tried using the AI google API from Google Images to do something like this?

Did you manage to find anything out?

Hope you are well,

Regards

Karl