gauss_filtergauss_filterGaussFilterGaussFiltergauss_filter (Operator)

Name

gauss_filtergauss_filterGaussFilterGaussFiltergauss_filter — Smooth using discrete Gauss functions.

Signature

Description

The operator gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter smoothes images using the discrete Gaussian, a discrete approximation of the Gaussian function,

The smoothing effect increases with increasing filter size. The following filter sizes (SizeSizeSizeSizesizesize) are supported (the sigma value of the Gauss function is indicated in brackets): 3 (0.600) 5 (1.075) 7 (1.550) 9 (2.025) 11 (2.550) For border treatment the gray values of the images are reflected at the image borders. Notice that, contrary to the operator gauss_imagegauss_imageGaussImageGaussImageGaussImagegauss_image, the relationship between the filter mask size and its respective value for the sigma parameter is linear.

The operator binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter can be used as an alternative to gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter. binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter is significantly faster than gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter. It should be noted that the mask size in binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter does not lead to the same amount of smoothing as the mask size in gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter. Corresponding mask sizes can be determined based on the respective values of the Gaussian smoothing parameter sigma.

gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter can be executed on OpenCL devices for all supported image types. However, the OpenCL implementation can produce slightly different results from the scalar implementation.

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

Attention

In order to be able to process gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter on an OpenCL device, ImageImageImageImageimageimage must be at least 64 pixels in both width and height.

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

• Supports OpenCL compute devices.
• 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

gauss_filter(Input,Gauss,7)
regiongrowing(Gauss,Segments,7,7,5,100)

gauss_filter(Input,&Gauss,7,);
regiongrowing(Gauss,&Segments,7,7,5,100,);

gauss_filter(Input,Gauss,7)
regiongrowing(Gauss,Segments,7,7,5,100)

gauss_filter(Input,Gauss,7)
regiongrowing(Gauss,Segments,7,7,5,100)

gauss_filter(Input,Gauss,7)
regiongrowing(Gauss,Segments,7,7,5,100)

Complexity

For each pixel: O(Size * 2).

Result

If the parameter values are correct the operator gauss_filtergauss_filterGaussFilterGaussFilterGaussFiltergauss_filter returns the value 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, grab_imagegrab_imageGrabImageGrabImageGrabImagegrab_image

Possible Successors

regiongrowingregiongrowingRegiongrowingRegiongrowingRegiongrowingregiongrowing, thresholdthresholdThresholdThresholdThresholdthreshold, sub_imagesub_imageSubImageSubImageSubImagesub_image, dyn_thresholddyn_thresholdDynThresholdDynThresholdDynThresholddyn_threshold, auto_thresholdauto_thresholdAutoThresholdAutoThresholdAutoThresholdauto_threshold

Alternatives

binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilterbinomial_filter, smooth_imagesmooth_imageSmoothImageSmoothImageSmoothImagesmooth_image, derivate_gaussderivate_gaussDerivateGaussDerivateGaussDerivateGaussderivate_gauss, isotropic_diffusionisotropic_diffusionIsotropicDiffusionIsotropicDiffusionIsotropicDiffusionisotropic_diffusion

See also

mean_imagemean_imageMeanImageMeanImageMeanImagemean_image, anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionAnisotropicDiffusionanisotropic_diffusion, sigma_imagesigma_imageSigmaImageSigmaImageSigmaImagesigma_image, gen_lowpassgen_lowpassGenLowpassGenLowpassGenLowpassgen_lowpass

Module

Foundation