ClassesClasses | | Operators

diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGauss (Operator)

Name

diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGauss — 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)

Description

diff_of_gaussdiff_of_gaussDiffOfGaussDiffOfGaussDiffOfGauss 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 (SigFactorSigFactorSigFactorSigFactorsigFactor) as:

For a SigFactor = 1.6, according to Marr, an approximation to the Mexican-Hat-Operator results. The resulting image is stored in DiffOfGaussDiffOfGaussDiffOfGaussDiffOfGaussdiffOfGauss.

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

Parameters

ImageImageImageImageimage (input_object)  (multichannel-)image(-array) objectHImageHImageHobject (byte / uint2)

Input image

DiffOfGaussDiffOfGaussDiffOfGaussDiffOfGaussdiffOfGauss (output_object)  (multichannel-)image(-array) objectHImageHImageHobject * (int2)

LoG image.

SigmaSigmaSigmaSigmasigma (input_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

Smoothing parameter of the Laplace operator to approximate.

Default value: 3.0

Suggested values: 2.0, 3.0, 4.0, 5.0

Typical range of values: 0.2 ≤ Sigma Sigma Sigma Sigma sigma ≤ 50.0

Minimum increment: 0.01

Recommended increment: 0.1

Restriction: Sigma > 0.0

SigFactorSigFactorSigFactorSigFactorsigFactor (input_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

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

Default value: 1.6

Typical range of values: 0.1 ≤ SigFactor SigFactor SigFactor SigFactor sigFactor ≤ 10.0

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)

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_gaussDiffOfGaussDiffOfGaussDiffOfGauss 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>)SetSystem("no_object_result",<Result>). If necessary, an exception is raised.

Possible Successors

zero_crossingzero_crossingZeroCrossingZeroCrossingZeroCrossing, dual_thresholddual_thresholdDualThresholdDualThresholdDualThreshold

Alternatives

laplacelaplaceLaplaceLaplaceLaplace, derivate_gaussderivate_gaussDerivateGaussDerivateGaussDerivateGauss

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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