Segment an image using regiongrowing.
::regiongrowing segments images into regions of the same intensity --- rastered into rectangles of size Row * Column. In order to decide whether two adjacent rectangles belong to the same region only the gray value of their center points is used. If the gray value difference is less then or equal to Tolerance the rectangles are merged into one region.
If g_{1} und g_{2} are two gray values to be examined, they are merged into the same region if:
|g_1 - g_2| < Tolerance
For images of type 'cyclic', the following formulas are used:
|g1 - g2| < Tolerance und |g1 - g2| <= 127
256 - |g1 - g2| < Tolerance und |g1 - g2| > 127
For rectangles larger than one pixel, ususally the images should be
smoothed with a lowpass filter with a size of at least Row * Column before calling
::regiongrowing (so that the gray values at the centers
of the regtangles are ``representative'' for the whole rectangle).
If the image contains little noise and the rectangles are small, the
smoothing can be omitted in many cases. This, of course, makes the
whole procedure faster.
The resulting regions are collections of rectangles of the chosen size Row * Column. Only regions containing at least MinSize points are returned.
Regiongrowing is a very fast operation, and thus suited for time-critical applications.
Column and Row are automatically converted to odd values if necessary.
|
Image (input_object) |
image(-array) -> Hobject: HImage(Array) ( byte / int1 / int2 / int4 / cyclic / real ) |
| Image to be segmented. | |
|
Regions (output_object) |
region-array -> Hobject * : HRegionArray |
| Extracted segments. | |
|
Row (input_control) |
extent.y -> HTuple.long |
| Vertical distance between tested pixels (height of the raster). | |
| Default value: 3 | |
| Suggested values: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 | |
| Typical range of values: 1 <= Row <= 99 (lin) | |
| Minimum increment: 2 | |
|
Recommended increment: 2 | |
| Restriction: (Row >= 1) && odd(Row) | |
|
Column (input_control) |
extent.x -> HTuple.long |
| Horizontal distance between tested pixels (height of the raster). | |
| Default value: 3 | |
| Suggested values: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 | |
| Typical range of values: 1 <= Column <= 99 (lin) | |
| Minimum increment: 2 | |
|
Recommended increment: 2 | |
| Restriction: (Column >= 1) && odd(Column) | |
|
Tolerance (input_control) |
number -> HTuple.double / long |
| Points with a gray value difference less then or equal to tolerance are accumulated into the same object. | |
| Default value: 6.0 | |
| Suggested values: 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.0, 14.0, 18.0, 25.0 | |
| Typical range of values: 1.0 <= Tolerance <= 127.0 (lin) | |
| Minimum increment: 0.01 | |
|
Recommended increment: 1.0 | |
| Restriction: (0 <= Tolerance) && (Tolerance < 127) | |
|
MinSize (input_control) |
integer -> HTuple.long |
| Minimum size of the output regions. | |
| Default value: 100 | |
| Suggested values: 1, 5, 10, 20, 50, 100, 200, 500, 1000 | |
| Typical range of values: 1 <= MinSize <= 1000 (lin) | |
| Minimum increment: 1 | |
|
Recommended increment: 5 | |
| Restriction: MinSize >= 1 | |
#include <iostream.h>
#include "HalconCpp.h"
int main (int argc, char *argv[])
{
if (argc != 2)
{
cout << "Usage : " << argv[0] << " <name of the image>" << endl;
return (-1);
}
HImage image (argv[1]),
mean;
HWindow win;
mean = image.MeanImage (5, 5);
HRegionArray reg = mean.Regiongrowing (5, 5, 6, 100);
reg.Display (win);
win.Click ();
return (0);
}
Let N be the number of found regions and M the number of points in one of these regions. Then the runtime complexity is O(N * log(M) * M).
::regiongrowing returns 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.
::mean_image, ::gauss_image, ::smooth_image, ::median_image, ::anisotrope_diff
::select_shape, ::reduce_domain, ::select_gray
::regiongrowing_n, ::regiongrowing_mean, ::label_to_region
Region processing
