Operator Reference

HALCON Operator Reference / Filters / Edges
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diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussdiff_of_gauss (Operator)

diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussdiff_of_gauss — Approximate the LoG operator (Laplace of Gaussian).

Signature

diff_of_gauss(Image : DiffOfGauss : Sigma, SigFactor : )

Herror diff_of_gauss(const Hobject Image, Hobject* DiffOfGauss, double Sigma, double SigFactor)

Herror T_diff_of_gauss(const Hobject Image, Hobject* DiffOfGauss, const Htuple Sigma, const Htuple SigFactor)

void DiffOfGauss(const HObject& Image, HObject* DiffOfGauss, const HTuple& Sigma, const HTuple& SigFactor)

HImage HImage::DiffOfGauss(double Sigma, double SigFactor) const

static void HOperatorSet.DiffOfGauss(HObject image, out HObject diffOfGauss, HTuple sigma, HTuple sigFactor)

HImage HImage.DiffOfGauss(double sigma, double sigFactor)

def diff_of_gauss(image: HObject, sigma: float, sig_factor: float) -> HObject

Description

diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussdiff_of_gauss approximates the Laplace-of-Gauss operator by a difference of Gaussians. The standard deviations of these Gaussians can be calculated, according to Marr, from the Parameter SigmaSigmaSigmasigmasigma of the LoG and the ratio of the two standard deviations (SigFactorSigFactorSigFactorsigFactorsig_factor) as:

For a , according to Marr, an approximation to the Mexican-Hat-Operator results. The resulting image is stored in DiffOfGaussDiffOfGaussDiffOfGaussdiffOfGaussdiff_of_gauss.

Attention

Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.

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.
  • Automatically parallelized on channel level.
  • Automatically parallelized on domain level.

Parameters

ImageImageImageimageimage (input_object)  (multichannel-)image(-array) → objectHImageHObjectHObjectHobject (byte / uint2)

Input image

DiffOfGaussDiffOfGaussDiffOfGaussdiffOfGaussdiff_of_gauss (output_object)  (multichannel-)image(-array) → objectHImageHObjectHObjectHobject * (int2)

LoG image.

SigmaSigmaSigmasigmasigma (input_control)  real → HTuplefloatHTupleHtuple (real) (double) (double) (double)

Smoothing parameter of the Laplace operator to approximate.

Default: 3.0

Suggested values: 2.0, 3.0, 4.0, 5.0

Minimum increment: 0.01

Recommended increment: 0.1

Restriction: Sigma > 0.0

SigFactorSigFactorSigFactorsigFactorsig_factor (input_control)  real → HTuplefloatHTupleHtuple (real) (double) (double) (double)

Ratio of the standard deviations used (Marr recommends 1.6).

Default: 1.6

Minimum increment: 0.01

Recommended increment: 0.1

Restriction: SigFactor > 0.0

Example (HDevelop)

read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)

Example (C)

read_image(&Image,"mreut");
diff_of_gauss(Image,&Laplace,2.0,1.6);
zero_crossing(Laplace,&ZeroCrossings);

Example (HDevelop)

read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)

Example (HDevelop)

read_image(Image,'fabrik')
diff_of_gauss(Image,Laplace,2.0,1.6)
zero_crossing(Laplace,ZeroCrossings)

Complexity

The execution time depends linearly on the number of pixels and the size of sigma.

Result

diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussdiff_of_gauss returns 2 ( H_MSG_TRUE) if all parameters are correct. If the input is empty the behavior can be set via set_system('no_object_result',<Result>)set_system("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>)set_system("no_object_result",<Result>). If necessary, an exception is raised.

Possible Successors

zero_crossingzero_crossingZeroCrossingZeroCrossingzero_crossing, dual_thresholddual_thresholdDualThresholdDualThresholddual_threshold

Alternatives

laplacelaplaceLaplaceLaplacelaplace, derivate_gaussderivate_gaussDerivateGaussDerivateGaussderivate_gauss

References

D. Marr: “Vision (A computational investigation into human representation and processing of visual information)”; New York, W.H. Freeman and Company; 1982.

Module

Foundation

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