HALCON 18.05 / HALCON Operator Reference / Image / Features
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fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeter (Operator)

Name

fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeter — Calculate the fuzzy perimeter of a region.

Signature

fuzzy_perimeter(Regions, Image : : Apar, Cpar : Perimeter)

Herror fuzzy_perimeter(const Hobject Regions, const Hobject Image, const Hlong Apar, const Hlong Cpar, double* Perimeter)

Herror T_fuzzy_perimeter(const Hobject Regions, const Hobject Image, const Htuple Apar, const Htuple Cpar, Htuple* Perimeter)

void FuzzyPerimeter(const HObject& Regions, const HObject& Image, const HTuple& Apar, const HTuple& Cpar, HTuple* Perimeter)

HTuple HImage::FuzzyPerimeter(const HRegion& Regions, Hlong Apar, Hlong Cpar) const

HTuple HRegion::FuzzyPerimeter(const HImage& Image, Hlong Apar, Hlong Cpar) const

static void HOperatorSet.FuzzyPerimeter(HObject regions, HObject image, HTuple apar, HTuple cpar, out HTuple perimeter)

HTuple HImage.FuzzyPerimeter(HRegion regions, int apar, int cpar)

HTuple HRegion.FuzzyPerimeter(HImage image, int apar, int cpar)

Description

The operator fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterFuzzyPerimeter 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 bandwith, and for x = b holds. In fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterFuzzyPerimeter, the parameters AparAparAparAparapar and CparCparCparCparcpar are defined as follows: b is .

Attention

Note that for fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterFuzzyPerimeter, the RegionsRegionsRegionsRegionsregions must lie completely within the previously defined domain. Otherwise an exception is raised.

Execution Information

Parameters

RegionsRegionsRegionsRegionsregions (input_object)  region(-array) → objectHRegionHRegionHobject

Regions for which the fuzzy perimeter is to be calculated.

ImageImageImageImageimage (input_object)  singlechannelimage → objectHImageHImageHobject (byte)

Input image containing the fuzzy membership values.

AparAparAparAparapar (input_control)  integer → HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Start of the fuzzy function.

Default value: 0

Suggested values: 0, 5, 10, 20, 50, 100

Typical range of values: 0 ≤ Apar Apar Apar Apar apar ≤ 255 (lin)

Minimum increment: 1

Recommended increment: 5

CparCparCparCparcpar (input_control)  integer → HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

End of the fuzzy function.

Default value: 255

Suggested values: 50, 100, 150, 200, 220, 255

Typical range of values: 0 ≤ Cpar Cpar Cpar Cpar cpar ≤ 255 (lin)

Minimum increment: 1

Recommended increment: 5

Restriction: Apar <= Cpar

PerimeterPerimeterPerimeterPerimeterperimeter (output_control)  real(-array) → HTupleHTupleHtuple (real) (double) (double) (double)

Fuzzy perimeter of a region.

Example (HDevelop)

* To find a Fuzzy Entropy from an Image
read_image(Image,'monkey')
fuzzy_perimeter(Trans,Trans,0,255,Per)

Example (C)

/* To find a Fuzzy Entropy from an Image */
read_image(&Image,"monkey");
fuzzy_perimeter(Trans,Trans,Apar,Bpar,&Per);

Example (HDevelop)

* To find a Fuzzy Entropy from an Image
read_image(Image,'monkey')
fuzzy_perimeter(Trans,Trans,0,255,Per)

Example (HDevelop)

* To find a Fuzzy Entropy from an Image
read_image(Image,'monkey')
fuzzy_perimeter(Trans,Trans,0,255,Per)

Example (HDevelop)

* To find a Fuzzy Entropy from an Image
read_image(Image,'monkey')
fuzzy_perimeter(Trans,Trans,0,255,Per)

Result

The operator fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterFuzzyPerimeter returns the value 2 (H_MSG_TRUE) if the parameters are correct. Otherwise an exception is raised.

See also

fuzzy_entropyfuzzy_entropyFuzzyEntropyFuzzyEntropyFuzzyEntropy

References

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.

Module

Foundation

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HALCON Operator Reference 18.05 Copyright © 1996-2018 MVTec Software GmbH