ClassesClasses | | Operators

points_foerstnerT_points_foerstnerPointsFoerstnerPointsFoerstner (Operator)

Name

points_foerstnerT_points_foerstnerPointsFoerstnerPointsFoerstner — Detect points of interest using the Förstner operator.

Signature

points_foerstner(Image : : SigmaGrad, SigmaInt, SigmaPoints, ThreshInhom, ThreshShape, Smoothing, EliminateDoublets : RowJunctions, ColumnJunctions, CoRRJunctions, CoRCJunctions, CoCCJunctions, RowArea, ColumnArea, CoRRArea, CoRCArea, CoCCArea)

Herror T_points_foerstner(const Hobject Image, const Htuple SigmaGrad, const Htuple SigmaInt, const Htuple SigmaPoints, const Htuple ThreshInhom, const Htuple ThreshShape, const Htuple Smoothing, const Htuple EliminateDoublets, Htuple* RowJunctions, Htuple* ColumnJunctions, Htuple* CoRRJunctions, Htuple* CoRCJunctions, Htuple* CoCCJunctions, Htuple* RowArea, Htuple* ColumnArea, Htuple* CoRRArea, Htuple* CoRCArea, Htuple* CoCCArea)

void PointsFoerstner(const HObject& Image, const HTuple& SigmaGrad, const HTuple& SigmaInt, const HTuple& SigmaPoints, const HTuple& ThreshInhom, const HTuple& ThreshShape, const HTuple& Smoothing, const HTuple& EliminateDoublets, HTuple* RowJunctions, HTuple* ColumnJunctions, HTuple* CoRRJunctions, HTuple* CoRCJunctions, HTuple* CoCCJunctions, HTuple* RowArea, HTuple* ColumnArea, HTuple* CoRRArea, HTuple* CoRCArea, HTuple* CoCCArea)

void HImage::PointsFoerstner(const HTuple& SigmaGrad, const HTuple& SigmaInt, const HTuple& SigmaPoints, const HTuple& ThreshInhom, double ThreshShape, const HString& Smoothing, const HString& EliminateDoublets, HTuple* RowJunctions, HTuple* ColumnJunctions, HTuple* CoRRJunctions, HTuple* CoRCJunctions, HTuple* CoCCJunctions, HTuple* RowArea, HTuple* ColumnArea, HTuple* CoRRArea, HTuple* CoRCArea, HTuple* CoCCArea) const

void HImage::PointsFoerstner(double SigmaGrad, double SigmaInt, double SigmaPoints, double ThreshInhom, double ThreshShape, const HString& Smoothing, const HString& EliminateDoublets, HTuple* RowJunctions, HTuple* ColumnJunctions, HTuple* CoRRJunctions, HTuple* CoRCJunctions, HTuple* CoCCJunctions, HTuple* RowArea, HTuple* ColumnArea, HTuple* CoRRArea, HTuple* CoRCArea, HTuple* CoCCArea) const

void HImage::PointsFoerstner(double SigmaGrad, double SigmaInt, double SigmaPoints, double ThreshInhom, double ThreshShape, const char* Smoothing, const char* EliminateDoublets, HTuple* RowJunctions, HTuple* ColumnJunctions, HTuple* CoRRJunctions, HTuple* CoRCJunctions, HTuple* CoCCJunctions, HTuple* RowArea, HTuple* ColumnArea, HTuple* CoRRArea, HTuple* CoRCArea, HTuple* CoCCArea) const

static void HOperatorSet.PointsFoerstner(HObject image, HTuple sigmaGrad, HTuple sigmaInt, HTuple sigmaPoints, HTuple threshInhom, HTuple threshShape, HTuple smoothing, HTuple eliminateDoublets, out HTuple rowJunctions, out HTuple columnJunctions, out HTuple coRRJunctions, out HTuple coRCJunctions, out HTuple coCCJunctions, out HTuple rowArea, out HTuple columnArea, out HTuple coRRArea, out HTuple coRCArea, out HTuple coCCArea)

void HImage.PointsFoerstner(HTuple sigmaGrad, HTuple sigmaInt, HTuple sigmaPoints, HTuple threshInhom, double threshShape, string smoothing, string eliminateDoublets, out HTuple rowJunctions, out HTuple columnJunctions, out HTuple coRRJunctions, out HTuple coRCJunctions, out HTuple coCCJunctions, out HTuple rowArea, out HTuple columnArea, out HTuple coRRArea, out HTuple coRCArea, out HTuple coCCArea)

void HImage.PointsFoerstner(double sigmaGrad, double sigmaInt, double sigmaPoints, double threshInhom, double threshShape, string smoothing, string eliminateDoublets, out HTuple rowJunctions, out HTuple columnJunctions, out HTuple coRRJunctions, out HTuple coRCJunctions, out HTuple coCCJunctions, out HTuple rowArea, out HTuple columnArea, out HTuple coRRArea, out HTuple coRCArea, out HTuple coCCArea)

Description

points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner extracts significant points from an image. Significant points are points that differ from their neighborhood, i.e., points where the image function changes in two dimensions. These changes occur on the one hand at the intersection of image edges (called junction points), and on the other hand at places where color or brightness differs from the surrounding neighborhood (called area points).

The point extraction takes place in two steps: In the first step the point regions, i.e., the inhomogeneous, isotropic regions, are extracted from the image. To do so, the smoothed matrix

is calculated, where and are the first derivatives of each image channel and S stands for a smoothing. If SmoothingSmoothingSmoothingSmoothingsmoothing is 'gauss'"gauss""gauss""gauss""gauss", the derivatives are computed with Gaussian derivatives of size SigmaGradSigmaGradSigmaGradSigmaGradsigmaGrad and the smoothing is performed by a Gaussian of size SigmaIntSigmaIntSigmaIntSigmaIntsigmaInt. If SmoothingSmoothingSmoothingSmoothingsmoothing is 'mean'"mean""mean""mean""mean", the derivatives are computed with a 3 x 3 Sobel filter (and hence SigmaGradSigmaGradSigmaGradSigmaGradsigmaGrad is ignored) and the smoothing is performed by a SigmaIntSigmaIntSigmaIntSigmaIntsigmaInt x SigmaIntSigmaIntSigmaIntSigmaIntsigmaInt mean filter. Then
inhomogeneity = Trace(M)
is the degree of inhomogeneity in the image and
is the degree of the isotropy of the texture in the image. Image points that have an inhomogeneity greater or equal to ThreshInhomThreshInhomThreshInhomThreshInhomthreshInhom and at the same time an isotropy greater or equal to ThreshShapeThreshShapeThreshShapeThreshShapethreshShape are subsequently examined further.

In the second step, two optimization functions are calculated for the resulting points. Essentially, these optimization functions average for each point the distances to the edge directions (for junction points) and the gradient directions (for area points) within an observation window around the point. If SmoothingSmoothingSmoothingSmoothingsmoothing is 'gauss'"gauss""gauss""gauss""gauss", the averaging is performed by a Gaussian of size SigmaPointsSigmaPointsSigmaPointsSigmaPointssigmaPoints, if SmoothingSmoothingSmoothingSmoothingsmoothing is 'mean'"mean""mean""mean""mean", the averaging is performed by a SigmaPointsSigmaPointsSigmaPointsSigmaPointssigmaPoints x SigmaPointsSigmaPointsSigmaPointsSigmaPointssigmaPoints mean filter. The local minima of the optimization functions determine the extracted points. Their subpixel precise position is returned in (RowJunctionsRowJunctionsRowJunctionsRowJunctionsrowJunctions, ColumnJunctionsColumnJunctionsColumnJunctionsColumnJunctionscolumnJunctions) and (RowAreaRowAreaRowAreaRowArearowArea, ColumnAreaColumnAreaColumnAreaColumnAreacolumnArea).

In addition to their position, for each extracted point the elements CoRRJunctionsCoRRJunctionsCoRRJunctionsCoRRJunctionscoRRJunctions, CoRCJunctionsCoRCJunctionsCoRCJunctionsCoRCJunctionscoRCJunctions, and CoCCJunctionsCoCCJunctionsCoCCJunctionsCoCCJunctionscoCCJunctions (and CoRRAreaCoRRAreaCoRRAreaCoRRAreacoRRArea, CoRCAreaCoRCAreaCoRCAreaCoRCAreacoRCArea, and CoCCAreaCoCCAreaCoCCAreaCoCCAreacoCCArea, respectively) of the corresponding covariance matrix are returned. This matrix facilitates conclusions about the precision of the calculated point position. To obtain the actual values, it is necessary to estimate the amount of noise in the input image and to multiply all components of the covariance matrix with the variance of the noise. (To estimate the amount of noise, apply intensityintensityIntensityIntensityIntensity to homogeneous image regions or plane_deviationplane_deviationPlaneDeviationPlaneDeviationPlaneDeviation to image regions, where the gray values form a plane. In both cases the amount of noise is returned in the parameter Deviation.) This is illustrated by the example program

%HALCONEXAMPLES%\hdevelop\Filter\Points\points_foerstner_ellipses.hdev
.

It lies in the nature of this operator that corners often result in two distinct points: One junction point, where the edges of the corner actually meet, and one area point inside the corner. Such doublets will be eliminated automatically, if EliminateDoubletsEliminateDoubletsEliminateDoubletsEliminateDoubletseliminateDoublets is 'true'"true""true""true""true". To do so, each pair of one junction point and one area point is examined. If the points lie within each others' observation window of the optimization function, for both points the precision of the point position is calculated and the point with the lower precision is rejected. If EliminateDoubletsEliminateDoubletsEliminateDoubletsEliminateDoubletseliminateDoublets is 'false'"false""false""false""false", every detected point is returned.

Attention

Note that only odd values for SigmaIntSigmaIntSigmaIntSigmaIntsigmaInt and SigmaPointsSigmaPointsSigmaPointsSigmaPointssigmaPoints are allowed, if SmoothingSmoothingSmoothingSmoothingsmoothing is 'mean'"mean""mean""mean""mean". Even values automatically will be replaced by the next larger odd value.

points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner with SmoothingSmoothingSmoothingSmoothingsmoothing = 'gauss'"gauss""gauss""gauss""gauss" uses a special implementation that is optimized using SSE2 instructions if the system parameter 'sse2_enable'"sse2_enable""sse2_enable""sse2_enable""sse2_enable" is set to 'true'"true""true""true""true" (which is default if SSE2 is available on your machine). This implementation is slightly inaccurate compared to the pure C version due to numerical issues (for 'byte' images the difference in RowJunctionsRowJunctionsRowJunctionsRowJunctionsrowJunctions and ColumnJunctionsColumnJunctionsColumnJunctionsColumnJunctionscolumnJunctions is in order of magnitude of 1.0e-5). If you prefer accuracy over performance you can set 'sse2_enable'"sse2_enable""sse2_enable""sse2_enable""sse2_enable" to 'false'"false""false""false""false" (using set_systemset_systemSetSystemSetSystemSetSystem) before you call points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner. This way points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner does not use SSE2 accelerations. Don't forget to set 'sse2_enable'"sse2_enable""sse2_enable""sse2_enable""sse2_enable" back to 'true'"true""true""true""true" afterwards.

Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.

Execution Information

Parameters

ImageImageImageImageimage (input_object)  (multichannel-)image objectHImageHImageHobject (byte / uint2 / real)

Input image.

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

Amount of smoothing used for the calculation of the gradient. If SmoothingSmoothingSmoothingSmoothingsmoothing is 'mean', SigmaGradSigmaGradSigmaGradSigmaGradsigmaGrad is ignored.

Default value: 1.0

Suggested values: 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 3.0

Typical range of values: 0.7 ≤ SigmaGrad SigmaGrad SigmaGrad SigmaGrad sigmaGrad ≤ 50.0

Recommended increment: 0.1

Restriction: SigmaGrad > 0.0

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

Amount of smoothing used for the integration of the gradients.

Default value: 2.0

Suggested values: 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 3.0

Typical range of values: 0.7 ≤ SigmaInt SigmaInt SigmaInt SigmaInt sigmaInt ≤ 50.0

Recommended increment: 0.1

Restriction: SigmaInt > 0.0

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

Amount of smoothing used in the optimization functions.

Default value: 3.0

Suggested values: 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0, 3.0

Typical range of values: 0.7 ≤ SigmaPoints SigmaPoints SigmaPoints SigmaPoints sigmaPoints ≤ 50.0

Recommended increment: 0.1

Restriction: SigmaPoints >= SigmaInt && SigmaPoints > 0.6

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

Threshold for the segmentation of inhomogeneous image areas.

Default value: 200

Suggested values: 50, 100, 200, 500, 1000

Restriction: ThreshInhom >= 0.0

ThreshShapeThreshShapeThreshShapeThreshShapethreshShape (input_control)  real HTupleHTupleHtuple (real) (double) (double) (double)

Threshold for the segmentation of point areas.

Default value: 0.3

Suggested values: 0.1, 0.2, 0.3, 0.4, 0.5, 0.7

Typical range of values: 0.01 ≤ ThreshShape ThreshShape ThreshShape ThreshShape threshShape ≤ 1

Minimum increment: 0.01

Recommended increment: 0.1

Restriction: 0.0 <= ThreshShape && ThreshShape <= 1.0

SmoothingSmoothingSmoothingSmoothingsmoothing (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Used smoothing method.

Default value: 'gauss' "gauss" "gauss" "gauss" "gauss"

List of values: 'gauss'"gauss""gauss""gauss""gauss", 'mean'"mean""mean""mean""mean"

EliminateDoubletsEliminateDoubletsEliminateDoubletsEliminateDoubletseliminateDoublets (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Elimination of multiply detected points.

Default value: 'false' "false" "false" "false" "false"

List of values: 'false'"false""false""false""false", 'true'"true""true""true""true"

RowJunctionsRowJunctionsRowJunctionsRowJunctionsrowJunctions (output_control)  point.y-array HTupleHTupleHtuple (real) (double) (double) (double)

Row coordinates of the detected junction points.

ColumnJunctionsColumnJunctionsColumnJunctionsColumnJunctionscolumnJunctions (output_control)  point.x-array HTupleHTupleHtuple (real) (double) (double) (double)

Column coordinates of the detected junction points.

CoRRJunctionsCoRRJunctionsCoRRJunctionsCoRRJunctionscoRRJunctions (output_control)  number-array HTupleHTupleHtuple (real) (double) (double) (double)

Row part of the covariance matrix of the detected junction points.

CoRCJunctionsCoRCJunctionsCoRCJunctionsCoRCJunctionscoRCJunctions (output_control)  number-array HTupleHTupleHtuple (real) (double) (double) (double)

Mixed part of the covariance matrix of the detected junction points.

CoCCJunctionsCoCCJunctionsCoCCJunctionsCoCCJunctionscoCCJunctions (output_control)  number-array HTupleHTupleHtuple (real) (double) (double) (double)

Column part of the covariance matrix of the detected junction points.

RowAreaRowAreaRowAreaRowArearowArea (output_control)  point.y-array HTupleHTupleHtuple (real) (double) (double) (double)

Row coordinates of the detected area points.

ColumnAreaColumnAreaColumnAreaColumnAreacolumnArea (output_control)  point.x-array HTupleHTupleHtuple (real) (double) (double) (double)

Column coordinates of the detected area points.

CoRRAreaCoRRAreaCoRRAreaCoRRAreacoRRArea (output_control)  number-array HTupleHTupleHtuple (real) (double) (double) (double)

Row part of the covariance matrix of the detected area points.

CoRCAreaCoRCAreaCoRCAreaCoRCAreacoRCArea (output_control)  number-array HTupleHTupleHtuple (real) (double) (double) (double)

Mixed part of the covariance matrix of the detected area points.

CoCCAreaCoCCAreaCoCCAreaCoCCAreacoCCArea (output_control)  number-array HTupleHTupleHtuple (real) (double) (double) (double)

Column part of the covariance matrix of the detected area points.

Result

points_foerstnerpoints_foerstnerPointsFoerstnerPointsFoerstnerPointsFoerstner returns 2 (H_MSG_TRUE) if all parameters are correct and no error occurs during the execution. If the input is empty the behavior can be set via set_system('no_object_result',<Result>)set_system("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>). If necessary, an exception is raised.

Possible Successors

gen_cross_contour_xldgen_cross_contour_xldGenCrossContourXldGenCrossContourXldGenCrossContourXld, disp_crossdisp_crossDispCrossDispCrossDispCross

Alternatives

points_harrispoints_harrisPointsHarrisPointsHarrisPointsHarris, points_lepetitpoints_lepetitPointsLepetitPointsLepetitPointsLepetit, points_harris_binomialpoints_harris_binomialPointsHarrisBinomialPointsHarrisBinomialPointsHarrisBinomial

References

W. Förstner, E. Gülch: “A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Circular features”. In Proceedings of the Intercommission Conference on Fast Processing of Photogrametric Data, Interlaken, pp. 281-305, 1987.
W. Förstner: “Statistische Verfahren für die automatische Bildanalyse und ihre Bewertung bei der Objekterkennung und -vermessung”. Volume 370, Series C, Deutsche Geodätische Kommission, München, 1991.
W. Förstner: “A Framework for Low Level Feature Extraction”. European Conference on Computer Vision, LNCS 802, pp. 383-394, Springer Verlag, 1994.
C. Fuchs: “Extraktion polymorpher Bildstrukturen und ihre topologische und geometrische Gruppierung”. Volume 502, Series C, Deutsche Geodätische Kommission, München, 1998.

Module

Foundation


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