Operator Reference
texture_laws (Operator)
texture_laws — Filter an image using a Laws texture filter.
Signature
texture_laws(Image : ImageTexture : FilterTypes, Shift, FilterSize : )
Description
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.
Attention
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.
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
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: '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: 2
Suggested values: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
FilterSize (input_control) integer → (integer)
Size of the filter kernel.
Default: 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_laws 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.
Possible Successors
mean_image,
binomial_filter,
gauss_filter,
median_image,
histo_2dim,
learn_ndim_norm,
threshold
Alternatives
See also
class_2dim_sup,
class_ndim_norm
References
Laws, Kenneth Ivan. “Textured Image Segmentation”; Ph.D. Thesis, Department of Electrical Engineering, Image Processing Institute, University of Southern California, 1980
Module
Foundation