I do think you are trying to solve the problem in a bit wrong way. It might sound groundless, so I’d better show my results first. Below I have a crop of you image on the left and discovered transplants on the right. Green color is used to highlight areas with more than one transplant. … Read more
The easy answer is don’t use a sequential model for this, use the functional API instead, implementing skip connections (also called residual connections) are then very easy, as shown in this example from the functional API guide: from keras.layers import merge, Convolution2D, Input # input tensor for a 3-channel 256×256 image x = Input(shape=(3, 256, … Read more
Very nice work on boundary detection. I used to work on similar segmentation problems. Theory: Once you obtained your edge map where e(i,j) indicates the “edge-iness” degree of pixel i,j you would like a segmentation of the image that would respect the edge map as possible. In order to formulate this “respect the edge map” … Read more
The model.evaluate function predicts the output for the given input and then computes the metrics function specified in the model.compile and based on y_true and y_pred and returns the computed metric value as the output. The model.predict just returns back the y_pred So if you use model.predict and then compute the metrics yourself, the computed … Read more
First of all: the function minMaxLoc finds only the global minimum and global maximum for a given input, so it is mostly useless for determining regional minima and/or regional maxima. But your idea is right, extracting markers based on regional minima/maxima for performing a Watershed Transform based on markers is totally fine. Let me try … Read more
“segmentation” is a partition of an image into several “coherent” parts, but without any attempt at understanding what these parts represent. One of the most famous works (but definitely not the first) is Shi and Malik “Normalized Cuts and Image Segmentation” PAMI 2000. These works attempt to define “coherence” in terms of low-level cues such … Read more
I’m Martin’s friend who was working on this earlier this year. This was my first ever coding project, and kinda ended in a bit of a rush, so the code needs some errr…decoding… I’ll give a few tips from what I’ve seen you doing already, and then sort my code on my day off tomorrow. … Read more
The basics first: The Mean Shift segmentation is a local homogenization technique that is very useful for damping shading or tonality differences in localized objects. An example is better than many words: Action:replaces each pixel with the mean of the pixels in a range-r neighborhood and whose value is within a distance d. The Mean … Read more