thresholding the maximum entropy

Maximum entropy thresholding is the maximization of information between object and background.
Updated 20 Feb 2012

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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)

Cite As

Fatma Gargouri (2024). thresholding the maximum entropy (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010b
Compatible with any release
Platform Compatibility
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