fuzzy_perimeter — Calculate the fuzzy perimeter of a region.
The operator fuzzy_perimeter is used to determine the
differences of fuzzy membership between an image point and its
neighbor points. The right and lower neighbor are taken into
account. The fuzzy perimeter is then defined as follows:
where MxN is the size of the image, and
u(x(m,n)) is the fuzzy membership function (i.e., the input
image). This implementation uses Zadeh's Standard-S function, which
is defined as follows:
The parameters a, b and c obey the following restrictions: is the inflection point of the
function, is the
bandwidth, and for x = b
holds. In fuzzy_perimeter, the parameters
Apar and Cpar are defined as follows: b is
.
Note that for fuzzy_perimeter, 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 perimeter 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: 0
Suggested values: 0, 5, 10, 20, 50, 100
Value range:
0
≤
Apar
≤
255 (lin)
Minimum increment: 1
Recommended increment: 5
Cpar (input_control) integer → (integer)
End of the fuzzy function.
Default: 255
Suggested values: 50, 100, 150, 200, 220, 255
Value range:
0
≤
Cpar
≤
255 (lin)
Minimum increment: 1
Recommended increment: 5
Restriction:
Apar <= Cpar
Perimeter (output_control) real(-array) → (real)
Fuzzy perimeter of a region.
* To find a Fuzzy Entropy from an Image read_image(Image,'monkey') fuzzy_perimeter(Trans,Trans,0,255,Per)
The operator fuzzy_perimeter 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