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texture_lawstexture_lawsTextureLawstexture_lawsTextureLawsTextureLaws (Operator)

Name

texture_lawstexture_lawsTextureLawstexture_lawsTextureLawsTextureLaws — Filter an image using a Laws texture filter.

Signature

texture_laws(Image : ImageTexture : FilterTypes, Shift, FilterSize : )

Herror texture_laws(const Hobject Image, Hobject* ImageTexture, const char* FilterTypes, const Hlong Shift, const Hlong FilterSize)

Herror T_texture_laws(const Hobject Image, Hobject* ImageTexture, const Htuple FilterTypes, const Htuple Shift, const Htuple FilterSize)

Herror texture_laws(Hobject Image, Hobject* ImageTexture, const HTuple& FilterTypes, const HTuple& Shift, const HTuple& FilterSize)

HImage HImage::TextureLaws(const HTuple& FilterTypes, const HTuple& Shift, const HTuple& FilterSize) const

HImageArray HImageArray::TextureLaws(const HTuple& FilterTypes, const HTuple& Shift, const HTuple& FilterSize) const

void TextureLaws(const HObject& Image, HObject* ImageTexture, const HTuple& FilterTypes, const HTuple& Shift, const HTuple& FilterSize)

HImage HImage::TextureLaws(const HString& FilterTypes, Hlong Shift, Hlong FilterSize) const

HImage HImage::TextureLaws(const char* FilterTypes, Hlong Shift, Hlong FilterSize) const

void HOperatorSetX.TextureLaws(
[in] IHUntypedObjectX* Image, [out] IHUntypedObjectX*ImageTexture, [in] VARIANT FilterTypes, [in] VARIANT Shift, [in] VARIANT FilterSize)

IHImageX* HImageX.TextureLaws(
[in] BSTR FilterTypes, [in] Hlong Shift, [in] Hlong FilterSize)

static void HOperatorSet.TextureLaws(HObject image, out HObject imageTexture, HTuple filterTypes, HTuple shift, HTuple filterSize)

HImage HImage.TextureLaws(string filterTypes, int shift, int filterSize)

Description

texture_lawstexture_lawsTextureLawstexture_lawsTextureLawsTextureLaws applies a texture transformation (according to Laws) to an image. This is done by convolving the input image with a special filter mask. The filters are:

9 different 3×3 matrices obtainable from the following three vectors:

    l      =       [  1     2       1  ],
    e      =       [ -1     0       1  ],
    s      =       [ -1     2      -1  ]
25 different 5×5 matrices obtainable from the following five vectors:
    l      =       [  1     4       6       4       1 ],
    e      =       [ -1    -2       0       2       1 ],
    s      =       [ -1     0       2       0      -1 ],
    w      =       [ -1     2       0      -2       1 ]
    r      =       [  1    -4       6      -4       1 ],
49 different 7×7 matrices obtainable from the following seven vectors:
    l      =       [  1     6      15      20      15      6      1  ],
    e      =       [ -1    -4      -5       0       5      4      1  ],
    s      =       [ -1    -2       1       4       1     -2     -1  ],
    w      =       [ -1     0       3       0      -3      0      1  ],
    r      =       [  1    -2      -1       4      -1     -2      1  ],
    u      =       [  1    -4       5       0      -5      4     -1  ]
    o      =       [ -1     6     -15      20     -15      6     -1  ]
The names of the filters are mnemonics for “level,” “edge,” “spot,” “wave,” “ripple,” “undulation,” and “oscillation.”

For most of the filters the resulting gray values must be modified by a ShiftShiftShiftShiftShiftshift. This makes the different textures in the output image more comparable to each other, provided suitable filters are used.

The name of the filter is composed of the letters of the two vectors used, where the first letter denotes convolution in the column direction while the second letter denotes convolution in the row direction.

Attention

texture_lawstexture_lawsTextureLawstexture_lawsTextureLawsTextureLaws can be executed on OpenCL devices.

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.

Parallelization

Parameters

ImageImageImageImageImageimage (input_object)  (multichannel-)image(-array) objectHImageHImageHImageHImageXHobject (byte* / int2* / uint2*) *allowed for compute devices

Images to which the texture transformation is to be applied.

ImageTextureImageTextureImageTextureImageTextureImageTextureimageTexture (output_object)  (multichannel-)image(-array) objectHImageHImageHImageHImageXHobject * (byte / int2 / uint2)

Texture images.

FilterTypesFilterTypesFilterTypesFilterTypesFilterTypesfilterTypes (input_control)  string HTupleHTupleHTupleVARIANTHtuple (string) (string) (HString) (char*) (BSTR) (char*)

Desired filter.

Default value: 'el' "el" "el" "el" "el" "el"

Suggested values: 'll'"ll""ll""ll""ll""ll", 'le'"le""le""le""le""le", 'ls'"ls""ls""ls""ls""ls", 'lw'"lw""lw""lw""lw""lw", 'lr'"lr""lr""lr""lr""lr", 'lu'"lu""lu""lu""lu""lu", 'lo'"lo""lo""lo""lo""lo", 'el'"el""el""el""el""el", 'ee'"ee""ee""ee""ee""ee", 'es'"es""es""es""es""es", 'ew'"ew""ew""ew""ew""ew", 'er'"er""er""er""er""er", 'eu'"eu""eu""eu""eu""eu", 'eo'"eo""eo""eo""eo""eo", 'sl'"sl""sl""sl""sl""sl", 'se'"se""se""se""se""se", 'ss'"ss""ss""ss""ss""ss", 'sw'"sw""sw""sw""sw""sw", 'sr'"sr""sr""sr""sr""sr", 'su'"su""su""su""su""su", 'so'"so""so""so""so""so", 'wl'"wl""wl""wl""wl""wl", 'we'"we""we""we""we""we", 'ws'"ws""ws""ws""ws""ws", 'ww'"ww""ww""ww""ww""ww", 'wr'"wr""wr""wr""wr""wr", 'wu'"wu""wu""wu""wu""wu", 'wo'"wo""wo""wo""wo""wo", 'rl'"rl""rl""rl""rl""rl", 're'"re""re""re""re""re", 'rs'"rs""rs""rs""rs""rs", 'rw'"rw""rw""rw""rw""rw", 'rr'"rr""rr""rr""rr""rr", 'ru'"ru""ru""ru""ru""ru", 'ro'"ro""ro""ro""ro""ro", 'ul'"ul""ul""ul""ul""ul", 'ue'"ue""ue""ue""ue""ue", 'us'"us""us""us""us""us", 'uw'"uw""uw""uw""uw""uw", 'ur'"ur""ur""ur""ur""ur", 'uu'"uu""uu""uu""uu""uu", 'uo'"uo""uo""uo""uo""uo", 'ol'"ol""ol""ol""ol""ol", 'oe'"oe""oe""oe""oe""oe", 'os'"os""os""os""os""os", 'ow'"ow""ow""ow""ow""ow", 'or'"or""or""or""or""or", 'ou'"ou""ou""ou""ou""ou", 'oo'"oo""oo""oo""oo""oo"

ShiftShiftShiftShiftShiftshift (input_control)  integer HTupleHTupleHTupleVARIANTHtuple (integer) (int / long) (Hlong) (Hlong) (Hlong) (Hlong)

Shift to reduce the gray value dynamics.

Default value: 2

Suggested values: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9

FilterSizeFilterSizeFilterSizeFilterSizeFilterSizefilterSize (input_control)  integer HTupleHTupleHTupleVARIANTHtuple (integer) (int / long) (Hlong) (Hlong) (Hlong) (Hlong)

Size of the filter kernel.

Default value: 5

List of values: 3, 5, 7

Example (HDevelop)

* Simple two-dimensional pixel classification
dev_get_window (WindowHandle)
read_image(Image,'combine')
texture_laws(Image,Texture1,'es',3,7)
texture_laws(Image,Texture2,'le',7,7)
MaskSize := 51
mean_image(Texture1,H1,MaskSize,MaskSize)
mean_image(Texture2,H2,MaskSize,MaskSize)
dev_clear_window ()
dev_display (Image)
dev_set_color ('green')
write_string (WindowHandle, 'Mark region within one texture area')
draw_region(Region,WindowHandle)
reduce_domain(H1,Region,Foreground1)
reduce_domain(H2,Region,Foreground2)
histo_2dim(Region,Foreground1,Foreground2,Histo)
get_image_size (Image, Width, Height)
threshold(Histo,Characteristic_area,1,Width*Height)
ShowIntermediateResult := 0
if (ShowIntermediateResult)
  histo_2dim(H1,H1,H2,HistoFull)
  dev_clear_window ()
  dev_set_lut ('sqrt')
  dev_display (HistoFull)
  dev_set_draw ('margin')
  dev_display (Characteristic_area)
  stop ()
  dev_set_lut ('default')
  dev_set_draw ('fill')
endif
class_2dim_sup(H1,H2,Characteristic_area,Seg)
dev_display (Image)
dev_set_color ('red')
dev_display (Seg)

Result

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

Possible Successors

mean_imagemean_imageMeanImagemean_imageMeanImageMeanImage, binomial_filterbinomial_filterBinomialFilterbinomial_filterBinomialFilterBinomialFilter, gauss_filtergauss_filterGaussFiltergauss_filterGaussFilterGaussFilter, median_imagemedian_imageMedianImagemedian_imageMedianImageMedianImage, histo_2dimhisto_2dimHisto2dimhisto_2dimHisto2dimHisto2dim, learn_ndim_normlearn_ndim_normLearnNdimNormlearn_ndim_normLearnNdimNormLearnNdimNorm, thresholdthresholdThresholdthresholdThresholdThreshold

Alternatives

convol_imageconvol_imageConvolImageconvol_imageConvolImageConvolImage

See also

class_2dim_supclass_2dim_supClass2dimSupclass_2dim_supClass2dimSupClass2dimSup, class_ndim_normclass_ndim_normClassNdimNormclass_ndim_normClassNdimNormClassNdimNorm

References

Laws, Kenneth Ivan. “Textured Image Segmentation”; Ph.D. Thesis, Department of Electrical Engineering, Image Processing Institute, University of Southern California, 1980

Module

Foundation


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