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fuzzy_entropy — Determine the fuzzy entropy of regions.
fuzzy_entropy calculates the fuzzy entropy of a fuzzy set. To do so, the image is regarded as a fuzzy set. The entropy then is a measure of how well the image approximates a white or black image. It is defined as follows:
1 ----
h(x) = --------- \ T (l) h(l)
M N ln(2) / e
----
where MxN is the size of the image, and h(l) is the histogram of the image. Furthermore,
T (l) = -u(l) ln(u(l)) - (1-u(l)) ln(1-u(l)) e
Here, u(x(m,n)) is a fuzzy membership function defining the fuzzy set (see fuzzy_perimeter). The same restrictions hold as in fuzzy_perimeter.
Regions for which the fuzzy entropy is to be calculated.
Input image containing the fuzzy membership values.
Start of the fuzzy function.
Default value: 0
Suggested values: 0, 5, 10, 20, 50, 100
Typical range of values: 0 ≤ Apar ≤ 255 (lin)
Minimum increment: 1
Recommended increment: 5
End of the fuzzy function.
Default value: 255
Suggested values: 50, 100, 150, 200, 220, 255
Typical range of values: 0 ≤ Cpar ≤ 255 (lin)
Minimum increment: 1
Recommended increment: 5
Restriction: Apar <= Cpar
Fuzzy entropy of a region.
* To find a Fuzzy Entropy from an Image read_image(Image,'monkey') fuzzy_entropy(Trans,Trans,0,255,Entro)
The operator fuzzy_entropy returns the value 2 (H_MSG_TRUE) if the parameters are correct. Otherwise an exception is raised.
M.K. Kundu, S.K. Pal: “Automatic selection of object enhancement operator with quantitative justification based on fuzzy set theoretic measures”; Pattern Recognition Letters 11; 1990; pp. 811-829.
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