sobel_dirsobel_dirSobelDirSobelDirsobel_dir calculates first derivative of an image and is
used as an edge detector. The filter is based on the following
filter masks:
A =
1 2 1
0 0 0
-1 -2 -1
B =
1 0 -1
2 0 -2
1 0 -1
These masks are used differently, according to the selected filter
type. (In the following, a and b denote the results of
convolving an image with A and B for one particular pixel.)
For a Sobel operator with size 3x3, the
corresponding filters A and B are applied directly, while for
larger filter sizes the input image is first smoothed using a
Gaussian filter (see gauss_imagegauss_imageGaussImageGaussImagegauss_image) or a binomial filter (see
binomial_filterbinomial_filterBinomialFilterBinomialFilterbinomial_filter) of size SizeSizeSizesizesize-2. The Gaussian
filter is selected for the above values of FilterTypeFilterTypeFilterTypefilterTypefilter_type.
Here, SizeSizeSizesizesize = 5, 7, 9, 11, or 13 must be used. The binomial
filter is selected by appending '_binomial'"_binomial""_binomial""_binomial""_binomial" to the above
values of FilterTypeFilterTypeFilterTypefilterTypefilter_type. Here, SizeSizeSizesizesize can be selected
between 5 and 39. Furthermore, it is possible to select different
amounts of smoothing the column and row direction by passing two
values in SizeSizeSizesizesize. Here, the first value of SizeSizeSizesizesize
corresponds to the mask width (smoothing in the column direction),
while the second value corresponds to the mask height (smoothing in
the row direction) of the binomial filter. The binomial filter can
only be used for images of type byte, uint2 and real. Since smoothing
reduces the edge amplitudes, in this case the edge amplitudes are
multiplied by a factor of 2 to prevent information loss. Therefore,
The edge directions are returned in EdgeDirectionEdgeDirectionEdgeDirectionedgeDirectionedge_direction, and
are stored in 2-degree steps, i.e., an edge direction of x
degrees in mathematically positive sense and with respect to the
horizontal axis is stored as x / 2
in the edge direction image. Furthermore, the direction
of the change of intensity is taken into account. Let
denote the image gradient. Then the
following edge directions are returned as :
intensity increase
edge direction
[deg]
from bottom to top
0 / +
0
from lower right to upper left
- / +
]0,90[
from right to left
- / 0
90
from upper right to lower left
- / -
]90,180[
from top to bottom
0 / -
180
from upper left to lower right
+ / -
]180,270[
from left to right
+ / 0
270
from lower left to upper right
+ / +
]270,360[.
Points with edge amplitude 0 are assigned the edge direction 255
(undefined direction).
List of values:
'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs", 'sum_abs_binomial'"sum_abs_binomial""sum_abs_binomial""sum_abs_binomial""sum_abs_binomial", 'sum_sqrt'"sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt", 'sum_sqrt_binomial'"sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial"
List of values (for compute devices):
'sum_abs'"sum_abs""sum_abs""sum_abs""sum_abs", 'sum_sqrt'"sum_sqrt""sum_sqrt""sum_sqrt""sum_sqrt", 'sum_abs_binomial'"sum_abs_binomial""sum_abs_binomial""sum_abs_binomial""sum_abs_binomial", 'sum_sqrt_binomial'"sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial""sum_sqrt_binomial"