dual_threshold🔗
Short description🔗
dual_threshold — Threshold operator for signed images.
Signature🔗
dual_threshold( image Image, out region RegionCrossings, integer MinSize, real MinGray, real Threshold )
Description🔗
dual_threshold segments the input image into a region with
gray values \(\ge\)Threshold (“positive”
regions) and a region with gray values \(\le-\)Threshold
(“negative” regions). Only “positive” or
“negative” regions having a size larger than MinSize are
taken into account. And regions whose maximum gray value is less than
MinGray in absolute value are suppressed.
The segmentation performed is not complete, i.e., the “positive”
and “negative” regions together do not necessarily cover the
entire image: Areas with a gray value between
\(-\)Threshold and
Threshold, \(-\)MinGray and
MinGray, respectively, are not taken into account.
dual_threshold is usually called after applying a Laplace
operator (laplace, laplace_of_gauss,
derivate_gauss or diff_of_gauss) to an image or with
the difference of two images (sub_image).
The zero crossings of a Laplace image correspond to edges in an
image, and are the separating regions of the “positive” and
“negative” regions in the Laplace image. They can be determined
by calling dual_threshold with Threshold = 1 and
then creating the complement regions with complement. The
parameter MinGray determines the noise invariance, while
MinSize determines the resolution of the edge detection.
Using byte images, only the positive part of the operator is
applied. Therefore dual_threshold behaves like a standard
threshold operator (threshold) with successive
connection and select_gray.
Execution information🔗
Execution information
-
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 internal data level.
Parameters🔗
Image (input_object) singlechannelimage(-array) → object (byte / int1 / int2 / int4 / real)
Input image.
RegionCrossings (output_object) region-array → object
Positive and negative regions.
MinSize (input_control) integer → (integer)
Regions smaller than MinSize are suppressed.
Default: 20
Suggested values: 0, 10, 20, 50, 100, 200, 500, 1000
Value range: 0 ≤ MinSize ≤ 10000 (lin)
Minimum increment: 1
Recommended increment: 10
MinGray (input_control) real → (real)
Regions whose maximum absolute gray value is smaller than MinGray are suppressed.
Default: 5.0
Suggested values: 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 11.0, 15.0, 20.0
Value range: 0 ≤ MinGray (lin)
Minimum increment: 1.0
Recommended increment: 10.0
Threshold (input_control) real → (real)
Regions that have a gray value smaller than Threshold (or larger than -Threshold) are suppressed.
Default: 2.0
Suggested values: 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 11.0, 15.0, 20.0
Value range: 0 ≤ Threshold (lin)
Minimum increment: 1.0
Recommended increment: 10.0
Example🔗
(HDevelop)
* Edge detection with the Laplace operator (and edge thinning)
diff_of_gauss(Image,Laplace,2.0,1.6)
* find "`positive"' and "`negative"' regions:
dual_threshold(Laplace,Region,20,2,1)
* The zero runnings are the complement to these image section:
complement(Region,ZeroCrossings)
* Simulation of dual_threshold
dual_threshold(Laplace,Result,MinS,MinG,Threshold)
threshold(Laplace,Tmp1,Threshold,999999)
connection(Tmp1,Tmp2)
select_shape(Tmp2,Tmp3,'area','and',MinS,999999)
select_gray(Laplace,Tmp3,Tmp4,'max','and',MinG,999999)
threshold(Laplace,Tmp5,-999999,-Threshold)
connection(Tmp5,Tmp6)
select_shape(Tmp6,Tmp7,'area','and',MinS,999999)
select_gray(Laplace,Tmp7,Tmp8,'min','and',-999999,-MinG)
concat_obj(Tmp4,Tmp8,Result)
/* Edge detection with the Laplace operator (and edge thinning) */
diff_of_gauss(Image,&Laplace,2.0,1.6)\;
/* find "`positive"' and "`negative"' regions: */
dual_threshold(Laplace,&Region,20,2,1)\;
/*The zero runnings are the complement to these image section: */
complement(Region,ZeroCrossings)\;
#include "HIOStream.h"
#if !defined(USE_IOSTREAM_H)
using namespace std\;
#endif
#include "HalconCpp.h"
using namespace Halcon\;
int main (int argc, char *argv[])
{
if (argc != 2)
{
cout << "Usage : " << argv[0] << " 'image' " << endl\;
return (-1)\;
}
HImage image (argv[1]),
laplace\;
HWindow win\;
HRegionArray region,
nulldg\;
image.Display (win)\;
laplace = image.DiffOfGauss (2.0, 1.6)\;
region = laplace.DualThreshold (20, 2, 1)\;
nulldg = region.Complement ()\;
laplace.Display (win)\; win.Click ()\;
region.Display (win)\; win.Click ()\;
nulldg.Display (win)\; win.Click ()\;
return (0)\;
}
Result🔗
dual_threshold returns 2 (H_MSG_TRUE) if all parameters are
correct. The behavior with respect to the input images and output
regions can be determined by setting the values of the flags
'no_object_result', 'empty_region_result', and
'store_empty_region' with set_system.
If necessary, an exception is raised.
Combinations with other operators🔗
Combinations
Possible predecessors
min_max_gray, sobel_amp, binomial_filter, gauss_filter, reduce_domain, diff_of_gauss, sub_image, derivate_gauss, laplace_of_gauss, laplace, expand_region
Possible successors
connection, dilation1, erosion1, opening, closing, rank_region, shape_trans, skeleton
Alternatives
threshold, dyn_threshold, check_difference
See also
Module🔗
Foundation