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:
where MxN is the size of the image, and h(l) is
the histogram of the image. Furthermore,
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.
Note that for fuzzy_entropy, the Regions must lie
completely within the previously defined domain. Otherwise an exception
is raised.
Regions (input_object) region(-array) → object
Regions for which the fuzzy entropy is to be calculated.
Image (input_object) singlechannelimage → object (byte)
Input image containing the fuzzy membership values.
Apar (input_control) integer → (integer)
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
Cpar (input_control) integer → (integer)
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
Entropy (output_control) real(-array) → (real)
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 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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