Hi folks, I am searching for the best way of doing blob detection in my noisy planetary image data but I just can't find the egg-laying woolen milk-giving pig (german proverb) that would solve all my problems, but then again it wouldn't be called research, if there's nothing to research, right? ;) In this case, I don't understand the working principle of the random_walker_segmentation example for one simple reason (i.e. 2 simple lines ;) ): markers[data < -0.3] = 1

markers[data > 1.3] = 2

Where do I get these thresholds from? In fact, finding reasonable thresholds that for sure contain the objects I am interested in is the whole problem, isn't it? If I have to read out 'reasonable' values from the histogram first, how would I do that? I played with 'significance' thresholding, something like dark pixels have to be x*sigma away from a median image pixel value, but then randomly-shaped shadows in noisy data that has no blobs are also being identified as blobs, which I need to avoid (see attached images with and without blobs). I also tried already to get rid of identified shadows using morphology operators (something like a proper blob has to survive 2 or 3 erosion operators) but this is not clean enough either. Can you maybe recommend a chain of image operations that would find me blobs in noisy data while having a low rate of false positives? Best regards, Michael PS.: I can't exclude that my brain is using a shape-related expectation about how the blobs have to look like, so maybe I have to put that into the game as well?