| Operators |
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.
Note that for fuzzy_entropy, the Regions must lie completely within the previously defined domain. Otherwise an exception is raised.
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.
Foundation
| Operators |