texture_lawsTextureLawsTextureLawstexture_lawstexture_laws🔗

Short description🔗

texture_lawsTextureLawsTextureLawstexture_lawstexture_laws — Filter an image using a Laws texture filter.

Signature🔗

texture_laws( image Image, out image ImageTexture, string FilterTypes, integer Shift, integer FilterSize )

Description🔗

texture_lawsTextureLaws 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 Shiftshiftshift. 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_lawsTextureLaws 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.

Execution information🔗

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🔗

Imageimageimage (input_object) (multichannel-)image(-array) → object (byte* / int2* / uint2*)HObject (byte* / int2* / uint2*)HImage (byte* / int2* / uint2*)HObject (byte* / int2* / uint2*)Hobject (byte* / int2* / uint2*) *allowed for compute devices

Images to which the texture transformation is to be applied.

ImageTextureimageTextureimage_texture (output_object) (multichannel-)image(-array) → object (byte / int2 / uint2)HObject (byte / int2 / uint2)HImage (byte / int2 / uint2)HObject (byte / int2 / uint2)Hobject * (byte / int2 / uint2)

Texture images.

FilterTypesfilterTypesfilter_types (input_control) string → (string)HTuple (HString)HTuple (string)strHtuple (char*)

Desired filter.

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

Shiftshiftshift (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Shift to reduce the gray value dynamics.

Default: 22
Suggested values: 0, 1, 2, 3, 4, 5, 6, 7, 8, 90, 1, 2, 3, 4, 5, 6, 7, 8, 9

FilterSizefilterSizefilter_size (input_control) integer → (integer)HTuple (Hlong)HTuple (int / long)intHtuple (Hlong)

Size of the filter kernel.

Default: 55
List of values: 3, 5, 73, 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_lawsTextureLaws 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>). If necessary, an exception is raised.

Combinations with other operators🔗

Combinations

Possible successors

mean_imageMeanImage, binomial_filterBinomialFilter, gauss_filterGaussFilter, median_imageMedianImage, histo_2dimHisto2dim, learn_ndim_normLearnNdimNorm, thresholdThreshold

Alternatives

convol_imageConvolImage

References🔗

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

Module🔗

Foundation