sigma_imagesigma_imageSigmaImageSigmaImagesigma_image (Operator)

Name

sigma_imagesigma_imageSigmaImageSigmaImagesigma_image — Non-linear smoothing with the sigma filter.

Signature

sigma_image(Image : ImageSigma : MaskHeight, MaskWidth, Sigma : )

Herror sigma_image(const Hobject Image, Hobject* ImageSigma, const Hlong MaskHeight, const Hlong MaskWidth, const Hlong Sigma)

Herror T_sigma_image(const Hobject Image, Hobject* ImageSigma, const Htuple MaskHeight, const Htuple MaskWidth, const Htuple Sigma)

void SigmaImage(const HObject& Image, HObject* ImageSigma, const HTuple& MaskHeight, const HTuple& MaskWidth, const HTuple& Sigma)

HImage HImage::SigmaImage(Hlong MaskHeight, Hlong MaskWidth, Hlong Sigma) const

static void HOperatorSet.SigmaImage(HObject image, out HObject imageSigma, HTuple maskHeight, HTuple maskWidth, HTuple sigma)

HImage HImage.SigmaImage(int maskHeight, int maskWidth, int sigma)

def sigma_image(image: HObject, mask_height: int, mask_width: int, sigma: int) -> HObject

Description

The operator sigma_imagesigma_imageSigmaImageSigmaImageSigmaImagesigma_image carries out a non-linear smoothing of the gray values of all input images (ImageImageImageImageimageimage). All pixels in a rectangular window (MaskHeight x MaskWidth) are used to determine the new gray value of the central pixel of this window. First, the gray value standard deviation of all pixels in the window is calculated. Then, all pixels of the window with a gray value that differs from the gray value of the central pixel by less than SigmaSigmaSigmaSigmasigmasigma times this standard deviation are used to calculate the new gray value of the central pixel. The gray value of the central pixel is the average of the gray values of the selected pixels. If no pixel could be selected for the averaging of the gray values, the gray value of the central pixel remains unchanged.

For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.

Attention

If even values instead of odd values are given for MaskHeightMaskHeightMaskHeightMaskHeightmaskHeightmask_height or MaskWidthMaskWidthMaskWidthMaskWidthmaskWidthmask_width, the routine uses the next larger odd values instead (this way the center of the filter mask is always explicitly determined).

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

ImageImageImageImageimageimage (input_object)  (multichannel-)image(-array) objectHImageHObjectHImageHobject (byte / cyclic / int1 / int2 / uint2 / int4 / real)

Image to be smoothed.

ImageSigmaImageSigmaImageSigmaImageSigmaimageSigmaimage_sigma (output_object)  (multichannel-)image(-array) objectHImageHObjectHImageHobject * (byte / cyclic / int1 / int2 / uint2 / int4 / real)

Smoothed image.

MaskHeightMaskHeightMaskHeightMaskHeightmaskHeightmask_height (input_control)  extent.y HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Height of the mask (number of lines).

Default value: 5

Suggested values: 3, 5, 7, 9, 11, 13, 15

Typical range of values: 3 ≤ MaskHeight MaskHeight MaskHeight MaskHeight maskHeight mask_height

Minimum increment: 2

Recommended increment: 2

Restriction: odd(MaskHeight)

MaskWidthMaskWidthMaskWidthMaskWidthmaskWidthmask_width (input_control)  extent.x HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Width of the mask (number of columns).

Default value: 5

Suggested values: 3, 5, 7, 9, 11, 13, 15

Typical range of values: 3 ≤ MaskWidth MaskWidth MaskWidth MaskWidth maskWidth mask_width

Minimum increment: 2

Recommended increment: 2

Restriction: odd(MaskWidth)

SigmaSigmaSigmaSigmasigmasigma (input_control)  integer HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Max. deviation to the average.

Default value: 3

Suggested values: 3, 5, 7, 9, 11, 20, 30, 50

Typical range of values: 0 ≤ Sigma Sigma Sigma Sigma sigma sigma

Minimum increment: 1

Recommended increment: 2

Example (HDevelop)

read_image(Image,'fabrik')
sigma_image(Image,ImageSigma,5,5,3)
dev_display(ImageSigma)

Example (C)

read_image(&Image,"fabrik");
sigma_image(Image,&ImageSigma,5,5,3);
disp_image(ImageSigma,WindowHandle);

Example (HDevelop)

read_image(Image,'fabrik')
sigma_image(Image,ImageSigma,5,5,3)
dev_display(ImageSigma)

Example (HDevelop)

read_image(Image,'fabrik')
sigma_image(Image,ImageSigma,5,5,3)
dev_display(ImageSigma)

Example (HDevelop)

read_image(Image,'fabrik')
sigma_image(Image,ImageSigma,5,5,3)
dev_display(ImageSigma)

Complexity

For each pixel: O(MaskHeight*MaskWidth).

Result

If the parameter values are correct the operator sigma_imagesigma_imageSigmaImageSigmaImageSigmaImagesigma_image returns the value 2 (H_MSG_TRUE). The behavior in case of empty input (no input images available) is set via the operator 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>)set_system("no_object_result",<Result>). If necessary an exception is raised.

Possible Predecessors

read_imageread_imageReadImageReadImageReadImageread_image

Possible Successors

thresholdthresholdThresholdThresholdThresholdthreshold, dyn_thresholddyn_thresholdDynThresholdDynThresholdDynThresholddyn_threshold, regiongrowingregiongrowingRegiongrowingRegiongrowingRegiongrowingregiongrowing

Alternatives

anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionAnisotropicDiffusionanisotropic_diffusion, rank_imagerank_imageRankImageRankImageRankImagerank_image

See also

smooth_imagesmooth_imageSmoothImageSmoothImageSmoothImagesmooth_image, binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter, gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter, mean_imagemean_imageMeanImageMeanImageMeanImagemean_image

References

R. Haralick, L. Shapiro; “Computer and Robot Vision”; Addison-Wesley, 1992, Seite 325

Module

Foundation