How can I use lorentzian norm in 2D gray scale image segmentation?
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I'm working on 2D image segmentation & I want to refine the image with lorentz as a preprocessing operation.
lorentzian norm equation is:
f(x)= sum(log(1+0.5(x/T))), where "x" is a distance.
my problem is how can I calculate the distance "x".
is it the distance between center pixel and just one neighbor?
or it's the distance between this pixel and its 8-neighbors?
"or is it the maximum or minimum distance"?
thanks
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Accepted Answer
Youssef Khmou
on 7 Sep 2013
Edited: Youssef Khmou
on 7 Sep 2013
rasha
Lorentizian metric requires 4 dimensions x,y,z,t, but here for image processing the matrix is 2D so then where there is sum in your Function replicate it to 2 sums , try to discuss this prototype :
X=im2double(imread('circuit.tif'));
T=norm(X) ; % random number chosen here to be euclidean norm
FX=sum(sum(log(1+0.5*X/T)))
More Answers (1)
Image Analyst
on 7 Sep 2013
I have no idea. If you don't either, then why are you so sure you want to do it?
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