Fit Gaussian to randomly distributed points
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I'm trying to recreate the signal produced by a microscope by randomly distributing x number of points(with a given intensity value) on a matrix of a size equal to the resolution(512). I then want to fit a gaussian on to each point to see at what density value of points do I lose the ability to resolve the peaks.
Regarding the code, I'm able to randomly allocate the points and set a peak intensity:
for i=1:number_of_points x=ceil(((b-a).*rand(i,1)+a)); y=ceil(((b-a).*rand(i,1)+a)); A(x,y)=100;
but I'm unable to fit gaussian to each one of those peaks. How would I take a single value in a matrix and fit a point spread function which caries over the entire matrix, until point density equals a value in which no peaks are resolvable
Greig on 26 May 2015
If I understand your question properly, I think imgaussfilt might be what you are looking for, but you will need to make some decision about the sigma value for the Gaussian. Also, I think it only does isotropic filtering (i.e., sigma is the same in all directions).