ClassesClasses | | Operators

regiongrowingregiongrowingRegiongrowingRegiongrowing (Operator)

Name

regiongrowingregiongrowingRegiongrowingRegiongrowing — Segment an image using regiongrowing.

Signature

regiongrowing(Image : Regions : Row, Column, Tolerance, MinSize : )

Herror regiongrowing(const Hobject Image, Hobject* Regions, const Hlong Row, const Hlong Column, double Tolerance, const Hlong MinSize)

Herror T_regiongrowing(const Hobject Image, Hobject* Regions, const Htuple Row, const Htuple Column, const Htuple Tolerance, const Htuple MinSize)

void Regiongrowing(const HObject& Image, HObject* Regions, const HTuple& Row, const HTuple& Column, const HTuple& Tolerance, const HTuple& MinSize)

HRegion HImage::Regiongrowing(Hlong Row, Hlong Column, const HTuple& Tolerance, Hlong MinSize) const

HRegion HImage::Regiongrowing(Hlong Row, Hlong Column, double Tolerance, Hlong MinSize) const

static void HOperatorSet.Regiongrowing(HObject image, out HObject regions, HTuple row, HTuple column, HTuple tolerance, HTuple minSize)

HRegion HImage.Regiongrowing(int row, int column, HTuple tolerance, int minSize)

HRegion HImage.Regiongrowing(int row, int column, double tolerance, int minSize)

Description

regiongrowingregiongrowingRegiongrowingRegiongrowingRegiongrowing 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 ToleranceToleranceToleranceTolerancetolerance 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 regiongrowingregiongrowingRegiongrowingRegiongrowingRegiongrowing (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 MinSizeMinSizeMinSizeMinSizeminSize points are returned.

Regiongrowing is a very fast operation, and thus suited for time-critical applications.

Attention

ColumnColumnColumnColumncolumn and RowRowRowRowrow are automatically converted to odd values if necessary.

Execution Information

Parameters

ImageImageImageImageimage (input_object)  singlechannelimage(-array) objectHImageHImageHobject (byte / direction / cyclic / int1 / int2 / int4 / real)

Input image.

RegionsRegionsRegionsRegionsregions (output_object)  region-array objectHRegionHRegionHobject *

Segmented regions.

RowRowRowRowrow (input_control)  extent.y HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

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 Row Row Row row ≤ 99 (lin)

Minimum increment: 2

Recommended increment: 2

Restriction: Row >= 1 && odd(Row)

ColumnColumnColumnColumncolumn (input_control)  extent.x HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

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 Column Column Column column ≤ 99 (lin)

Minimum increment: 2

Recommended increment: 2

Restriction: Column >= 1 && odd(Column)

ToleranceToleranceToleranceTolerancetolerance (input_control)  number HTupleHTupleHtuple (real / integer) (double / int / long) (double / Hlong) (double / Hlong)

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 Tolerance Tolerance Tolerance tolerance ≤ 127.0 (lin)

Minimum increment: 0.01

Recommended increment: 1.0

Restriction: 0 <= Tolerance && Tolerance < 127

MinSizeMinSizeMinSizeMinSizeminSize (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

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 MinSize MinSize MinSize minSize

Minimum increment: 1

Recommended increment: 5

Restriction: MinSize >= 1

Example (HDevelop)

read_image(Image,'fabrik')
mean_image(Image,Mean,Row,Column)
regiongrowing(Mean,Result,Row,Column,6.0,100)

Example (C)

read_image(&Image,"fabrik");
mean_image(Image,&Mean,Row,Column);
regiongrowing(Mean,&Result,Row,Column,6,100);

Example (HDevelop)

read_image(Image,'fabrik')
mean_image(Image,Mean,Row,Column)
regiongrowing(Mean,Result,Row,Column,6.0,100)

Example (HDevelop)

read_image(Image,'fabrik')
mean_image(Image,Mean,Row,Column)
regiongrowing(Mean,Result,Row,Column,6.0,100)

Example (HDevelop)

read_image(Image,'fabrik')
mean_image(Image,Mean,Row,Column)
regiongrowing(Mean,Result,Row,Column,6.0,100)

Complexity

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).

Result

regiongrowingregiongrowingRegiongrowingRegiongrowingRegiongrowing 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'"no_object_result""no_object_result""no_object_result""no_object_result", 'empty_region_result'"empty_region_result""empty_region_result""empty_region_result""empty_region_result", and 'store_empty_region'"store_empty_region""store_empty_region""store_empty_region""store_empty_region" with set_systemset_systemSetSystemSetSystemSetSystem. If necessary, an exception is raised.

Possible Predecessors

binomial_filterbinomial_filterBinomialFilterBinomialFilterBinomialFilter, mean_imagemean_imageMeanImageMeanImageMeanImage, gauss_filtergauss_filterGaussFilterGaussFilterGaussFilter, smooth_imagesmooth_imageSmoothImageSmoothImageSmoothImage, median_imagemedian_imageMedianImageMedianImageMedianImage, anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusionAnisotropicDiffusion

Possible Successors

select_shapeselect_shapeSelectShapeSelectShapeSelectShape, reduce_domainreduce_domainReduceDomainReduceDomainReduceDomain, select_grayselect_graySelectGraySelectGraySelectGray

Alternatives

regiongrowing_nregiongrowing_nRegiongrowingNRegiongrowingNRegiongrowingN, regiongrowing_meanregiongrowing_meanRegiongrowingMeanRegiongrowingMeanRegiongrowingMean, label_to_regionlabel_to_regionLabelToRegionLabelToRegionLabelToRegion

Module

Foundation


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