texture_laws — 使用劳斯（Laws）纹理过滤器过滤图像。
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 可在 OpenCL 设备上执行。
请注意，若使用域缩减后的图像作为输入，滤波器算子可能会返回意外结果。请参阅 滤波器 一章
Image (输入对象) (multichannel-)image(-array) → object (byte* / int2* / uint2*) *允许用于计算设备
Images to which the texture transformation is to be applied.
ImageTexture (输出对象) (multichannel-)image(-array) → object (byte / int2 / uint2)
Texture images.
FilterTypes (输入控制) string → (string)
Desired filter.
默认值： 'el'
建议值： '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 (输入控制) integer → (integer)
Shift to reduce the gray value dynamics.
默认值： 2
建议值： 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
FilterSize (输入控制) integer → (integer)
Size of the filter kernel.
默认值： 5
值列表： 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 在所有参数正确时返回 2 ( H_MSG_TRUE )。 如果输入为空则可设置行为通过 set_system('no_object_result',<Result>)。如有必要，则抛出异常。
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
基础