Operator Reference

HALCON Operator Reference / Image / Features
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fuzzy_entropyfuzzy_entropyFuzzyEntropyFuzzyEntropyfuzzy_entropy (Operator)

fuzzy_entropyfuzzy_entropyFuzzyEntropyFuzzyEntropyfuzzy_entropy — Determine the fuzzy entropy of regions.

Signature

fuzzy_entropy(Regions, Image : : Apar, Cpar : Entropy)

Herror fuzzy_entropy(const Hobject Regions, const Hobject Image, const Hlong Apar, const Hlong Cpar, double* Entropy)

Herror T_fuzzy_entropy(const Hobject Regions, const Hobject Image, const Htuple Apar, const Htuple Cpar, Htuple* Entropy)

void FuzzyEntropy(const HObject& Regions, const HObject& Image, const HTuple& Apar, const HTuple& Cpar, HTuple* Entropy)

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

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

static void HOperatorSet.FuzzyEntropy(HObject regions, HObject image, HTuple apar, HTuple cpar, out HTuple entropy)

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

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

def fuzzy_entropy(regions: HObject, image: HObject, apar: int, cpar: int) -> Sequence[float]

def fuzzy_entropy_s(regions: HObject, image: HObject, apar: int, cpar: int) -> float

Description

fuzzy_entropyfuzzy_entropyFuzzyEntropyFuzzyEntropyfuzzy_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_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterfuzzy_perimeter). The same restrictions hold as in fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterfuzzy_perimeter.

Attention

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

Execution Information

  • Multithreading type: reentrant (runs in parallel with non-exclusive operators).
  • Multithreading scope: global (may be called from any thread).
  • Automatically parallelized on tuple level.

Parameters

RegionsRegionsRegionsregionsregions (input_object)  region(-array) → objectHRegionHObjectHObjectHobject

Regions for which the fuzzy entropy is to be calculated.

ImageImageImageimageimage (input_object)  singlechannelimage → objectHImageHObjectHObjectHobject (byte)

Input image containing the fuzzy membership values.

AparAparAparaparapar (input_control)  integer → HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Start of the fuzzy function.

Default: 0

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

Value range: 0 ≤ Apar Apar Apar apar apar ≤ 255 (lin)

Minimum increment: 1

Recommended increment: 5

CparCparCparcparcpar (input_control)  integer → HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

End of the fuzzy function.

Default: 255

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

Value range: 0 ≤ Cpar Cpar Cpar cpar cpar ≤ 255 (lin)

Minimum increment: 1

Recommended increment: 5

Restriction: Apar <= Cpar

EntropyEntropyEntropyentropyentropy (output_control)  real(-array) → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Fuzzy entropy of a region.

Example (HDevelop)

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

Example (C)

/* To find a Fuzzy Entropy from an Image */
read_image(&Image,'monkey');
fuzzy_entropy(Trans,Trans,0,255,&Entro);

Example (HDevelop)

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

Example (HDevelop)

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

Result

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

See also

fuzzy_perimeterfuzzy_perimeterFuzzyPerimeterFuzzyPerimeterfuzzy_perimeter

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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