fuzzy_entropy — 确定区域的模糊熵。
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.否则将抛出异常。
Regions (输入对象) region(-array) → object
Regions for which the fuzzy entropy is to be calculated.
Image (输入对象) singlechannelimage → object (byte)
Input image containing the fuzzy membership values.
Apar (输入控制) integer → (integer)
Start of the fuzzy function.
默认值: 0
建议值: 0, 5, 10, 20, 50, 100
值范围:
0
≤
Apar
≤
255 (lin)
最小增量: 1
建议增量: 5
Cpar (输入控制) integer → (integer)
End of the fuzzy function.
默认值: 255
建议值: 50, 100, 150, 200, 220, 255
值范围:
0
≤
Cpar
≤
255 (lin)
最小增量: 1
建议增量: 5
限制:
Apar <= Cpar
Entropy (输出控制) 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)
算子 fuzzy_entropy 在参数正确时返回值 2 ( H_MSG_TRUE )。否则将抛出异常。
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.
基础