Maximum entropy thresholding is based on the maximization of the information measure between object and background.
let C1 and C2 two classes for the object and the background respectively; the maximum entropy measure can be calculated :
hC1(t)= - sum (pi/pC1)*log(pi/pC1) for i<=t
hC2(t)= - sum (pi/pC2)*log(pi/pC2) for i>t
pC1=sum pi i<=t and pC2=sum pi i>t
pC1+pC2=1 because the histogram is normalized
pi estimate the probability of the gray-level value "i"
where ni is the occurrence of the gray level "i" in the image.
ni is the histogram h(i)
Fatma Gargouri (2022). thresholding the maximum entropy (https://www.mathworks.com/matlabcentral/fileexchange/35158-thresholding-the-maximum-entropy), MATLAB Central File Exchange. Retrieved .
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