texture_laws — Filter an image using a Laws texture filter.
texture_laws(Image : ImageTexture : FilterTypes, Shift, FilterSize : )
texture_laws 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 Shift. 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.
texture_laws 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.
Image (input_object) (multichannel-)image(-array) → object (byte* / int2* / uint2*) *allowed for compute devices
Images to which the texture transformation is to be applied.
ImageTexture (output_object) (multichannel-)image(-array) → object (byte / int2 / uint2)
Texture images.
FilterTypes (input_control) string → (string)
Desired filter.
Default value: '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'
Shift (input_control) integer → (integer)
Shift to reduce the gray value dynamics.
Default value: 2
Suggested values: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
FilterSize (input_control) integer → (integer)
Size of the filter kernel.
Default value: 5
List of values: 3, 5, 7
* 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)
texture_laws returns 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.
mean_image,
binomial_filter,
gauss_filter,
median_image,
histo_2dim,
learn_ndim_norm,
threshold
class_2dim_sup,
class_ndim_norm
Laws, Kenneth Ivan. “Textured Image Segmentation”; Ph.D. Thesis, Department of Electrical Engineering, Image Processing Institute, University of Southern California, 1980
Foundation