Filter an image using a Laws texture filter.
texture_laws applies a texture transformations (according to Laws) to an image. This is done by convolving the input image with a special filter mask. The filters are:
9 different 3x3 matrices obtainable from the following three vectors:
l = [ 1 2 1 ],
e = [ -1 0 1 ],
s = [ -1 2 -1 ]
25 different 5x5 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 ],
r = [ 1 -4 6 -4 1 ],
w = [ -1 2 0 -2 1 ]
36 different 7x7 matrices obtainable from the following six 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 ],
r = [ -1 -2 -1 4 -1 -2 1 ],
w = [ -1 0 3 0 -3 0 1 ],
o = [ -1 6 -15 20 -15 6 -1 ]
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.
|
Image (input_object) |
(multichannel-)image(-array) -> object : byte / int2 / uint2 |
| 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(-array) -> string |
| Desired filters (name or number). | |
| Default value: 'el' | |
| Suggested values: 'll', 'le', 'ls', 'lr', 'lw', 'lo', 'el', 'ee', 'es', 'er', 'ew', 'eo', 'sl', 'se', 'ss', 'sr', 'sw', 'so', 'rl', 're', 'rs', 'rr', 'rw', 'ro', 'wl', 'we', 'ws', 'wr', 'ww', 'wo', 'ol', 'oe', 'os', 'or', 'ow', 'oo' | |
|
Shift (input_control) |
integer -> integer |
| Shift to reduce the gray value dynamics. | |
| Default value: 2 | |
| List of 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 | |
/* Two-dimensional pixel classification */ read_image(Image,'combine') open_window(0,0,-1,-1,'root','visible','',WindowHandle) disp_image(Image,WindowHandle) texture_laws(Image,Texture1,'es',2,5) texture_laws(Image,Texture2,'le',2,5) mean_image(Texture1,H1,51,51) mean_image(Texture2,H2,51,51) fwrite_string(FileId,'mark desired image section') fnew_line(FileId) set_color(WindowHandle,'green') draw_region(Region,WindowHandle) reduce_domain(H1,Region,Foreground1) reduce_domain(H2,Region,Foreground2) histo_2dim(Region,Foreground1,Foreground2,Histo) threshold(Histo,Characteristic_area,1,1000000) set_color(WindowHandle,'blue') disp_region(Characteristic_area,WindowHandle) class_2dim_sup(H1,H2,Characteristic_area,Seg,4,5) set_color(WindowHandle,'red') disp_region(Seg,WindowHandle).
texture_laws 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>). If necessary, an exception handling is raised.
texture_laws is reentrant and automatically parallelized (on tuple level, channel level, domain level).
mean_image, gauss_image, median_image, histo_2dim, learn_ndim_norm, learn_ndim_box, threshold
class_2dim_sup, class_ndim_norm
Laws, K.I. ``Textured image segmentation''; Ph.D. dissertation, Dept. of Engineering, Univ. Southern California, 1980
Foundation
