regiongrowing — 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} and g_{2} are two gray values to be examined, they are
merged into the same region if:
For images of type 'cyclic', the following formulas are used:
For rectangles larger than one pixel, usually 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 rectangles 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.
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) singlechannelimage(-array) → object (byte / direction / cyclic / int1 / int2 / int4 / real)
Input image.
Regions (output_object) region-array → object
Segmented regions.
Row (input_control) extent.y → (integer)
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 → (integer)
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 → (real / integer)
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 → (integer)
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
Minimum increment: 1
Recommended increment: 5
Restriction: MinSize >= 1
read_image(Image,'fabrik') mean_image(Image,Mean,Row,Column) regiongrowing(Mean,Result,Row,Column,6.0,100)
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 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.
binomial_filter,
mean_image,
gauss_filter,
smooth_image,
median_image,
anisotropic_diffusion
select_shape,
reduce_domain,
select_gray
regiongrowing_n,
regiongrowing_mean,
label_to_region
Foundation