Name
points_foerstner T_points_foerstner PointsFoerstner points_foerstner PointsFoerstner PointsFoerstner — Detect points of interest using the Förstner operator.
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 )
Herror points_foerstner (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 )
HTuple HImage ::PointsFoerstner (const HTuple& SigmaGrad , const HTuple& SigmaInt , const HTuple& SigmaPoints , const HTuple& ThreshInhom , const HTuple& ThreshShape , const HTuple& Smoothing , const HTuple& EliminateDoublets , HTuple* ColumnJunctions , HTuple* CoRRJunctions , HTuple* CoRCJunctions , HTuple* CoCCJunctions , HTuple* RowArea , HTuple* ColumnArea , HTuple* CoRRArea , HTuple* CoRCArea , HTuple* CoCCArea ) const
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
void HOperatorSetX .PointsFoerstner ( [in] IHUntypedObjectX* Image , [in] VARIANT SigmaGrad , [in] VARIANT SigmaInt , [in] VARIANT SigmaPoints , [in] VARIANT ThreshInhom , [in] VARIANT ThreshShape , [in] VARIANT Smoothing , [in] VARIANT EliminateDoublets , [out] VARIANT* RowJunctions , [out] VARIANT* ColumnJunctions , [out] VARIANT* CoRRJunctions , [out] VARIANT* CoRCJunctions , [out] VARIANT* CoCCJunctions , [out] VARIANT* RowArea , [out] VARIANT* ColumnArea , [out] VARIANT* CoRRArea , [out] VARIANT* CoRCArea , [out] VARIANT* CoCCArea )
VARIANT HImageX .PointsFoerstner ( [in] VARIANT SigmaGrad , [in] VARIANT SigmaInt , [in] VARIANT SigmaPoints , [in] VARIANT ThreshInhom , [in] double ThreshShape , [in] BSTR Smoothing , [in] BSTR EliminateDoublets , [out] VARIANT* ColumnJunctions , [out] VARIANT* CoRRJunctions , [out] VARIANT* CoRCJunctions , [out] VARIANT* CoCCJunctions , [out] VARIANT* RowArea , [out] VARIANT* ColumnArea , [out] VARIANT* CoRRArea , [out] VARIANT* CoRCArea , [out] VARIANT* CoCCArea )
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 )
points_foerstner points_foerstner PointsFoerstner points_foerstner PointsFoerstner PointsFoerstner 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
Smoothing Smoothing Smoothing Smoothing Smoothing smoothing is 'gauss' "gauss" "gauss" "gauss" "gauss" "gauss" , the derivatives are computed with
Gaussian derivatives of size SigmaGrad SigmaGrad SigmaGrad SigmaGrad SigmaGrad sigmaGrad and the smoothing is
performed by a Gaussian of size SigmaInt SigmaInt SigmaInt SigmaInt SigmaInt sigmaInt . If Smoothing Smoothing Smoothing Smoothing Smoothing smoothing
is 'mean' "mean" "mean" "mean" "mean" "mean" , the derivatives are computed with a
3 x 3 Sobel filter (and hence SigmaGrad SigmaGrad SigmaGrad SigmaGrad SigmaGrad sigmaGrad is ignored)
and the smoothing is performed by a SigmaInt SigmaInt SigmaInt SigmaInt SigmaInt sigmaInt x SigmaInt SigmaInt SigmaInt SigmaInt SigmaInt sigmaInt 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 ThreshInhom ThreshInhom ThreshInhom ThreshInhom ThreshInhom threshInhom and at the
same time an isotropy greater or equal to ThreshShape ThreshShape ThreshShape ThreshShape ThreshShape threshShape 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 Smoothing Smoothing Smoothing Smoothing Smoothing smoothing is 'gauss' "gauss" "gauss" "gauss" "gauss" "gauss" , the averaging is
performed by a Gaussian of size SigmaPoints SigmaPoints SigmaPoints SigmaPoints SigmaPoints sigmaPoints , if Smoothing Smoothing Smoothing Smoothing Smoothing smoothing
is 'mean' "mean" "mean" "mean" "mean" "mean" , the averaging is performed by a SigmaPoints SigmaPoints SigmaPoints SigmaPoints SigmaPoints sigmaPoints x SigmaPoints SigmaPoints SigmaPoints SigmaPoints SigmaPoints sigmaPoints
mean filter. The local minima of the optimization
functions determine the extracted points. Their subpixel precise position is
returned in (RowJunctions RowJunctions RowJunctions RowJunctions RowJunctions rowJunctions , ColumnJunctions ColumnJunctions ColumnJunctions ColumnJunctions ColumnJunctions columnJunctions ) and
(RowArea RowArea RowArea RowArea RowArea rowArea , ColumnArea ColumnArea ColumnArea ColumnArea ColumnArea columnArea ).
In addition to their position, for each extracted point the elements
CoRRJunctions CoRRJunctions CoRRJunctions CoRRJunctions CoRRJunctions coRRJunctions , CoRCJunctions CoRCJunctions CoRCJunctions CoRCJunctions CoRCJunctions coRCJunctions , and CoCCJunctions CoCCJunctions CoCCJunctions CoCCJunctions CoCCJunctions coCCJunctions
(and CoRRArea CoRRArea CoRRArea CoRRArea CoRRArea coRRArea , CoRCArea CoRCArea CoRCArea CoRCArea CoRCArea coRCArea , and CoCCArea CoCCArea CoCCArea CoCCArea CoCCArea coCCArea ,
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 intensity intensity Intensity intensity Intensity Intensity to homogeneous image regions or
plane_deviation plane_deviation PlaneDeviation plane_deviation PlaneDeviation PlaneDeviation 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 EliminateDoublets EliminateDoublets EliminateDoublets EliminateDoublets EliminateDoublets eliminateDoublets is 'true' "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
EliminateDoublets EliminateDoublets EliminateDoublets EliminateDoublets EliminateDoublets eliminateDoublets is 'false' "false" "false" "false" "false" "false" , every detected point is
returned.
Note that only odd values for SigmaInt SigmaInt SigmaInt SigmaInt SigmaInt sigmaInt and
SigmaPoints SigmaPoints SigmaPoints SigmaPoints SigmaPoints sigmaPoints are allowed, if Smoothing Smoothing Smoothing Smoothing Smoothing smoothing is
'mean' "mean" "mean" "mean" "mean" "mean" . Even values automatically will be replaced by the
next larger odd value.
points_foerstner points_foerstner PointsFoerstner points_foerstner PointsFoerstner PointsFoerstner with Smoothing Smoothing Smoothing Smoothing Smoothing smoothing = 'gauss' "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" "sse2_enable" is set to 'true' "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 RowJunctions RowJunctions RowJunctions RowJunctions RowJunctions rowJunctions and
ColumnJunctions ColumnJunctions ColumnJunctions ColumnJunctions ColumnJunctions columnJunctions 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" "sse2_enable" to
'false' "false" "false" "false" "false" "false" (using set_system set_system SetSystem set_system SetSystem SetSystem ) before you call
points_foerstner points_foerstner PointsFoerstner points_foerstner PointsFoerstner PointsFoerstner . This way points_foerstner points_foerstner PointsFoerstner points_foerstner PointsFoerstner PointsFoerstner does not use
SSE2 accelerations. Don't forget to set 'sse2_enable' "sse2_enable" "sse2_enable" "sse2_enable" "sse2_enable" "sse2_enable" back
to 'true' "true" "true" "true" "true" "true" afterwards.
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Automatically parallelized on internal data level.
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
sigmaInt
≤
50.0
Recommended increment: 0.1
Restriction: SigmaInt > 0.0
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
sigmaPoints
≤
50.0
Recommended increment: 0.1
Restriction: SigmaPoints >= SigmaInt && SigmaPoints > 0.6
Threshold for the segmentation of inhomogeneous image
areas.
Default value: 200
Suggested values: 50, 100, 200, 500, 1000
Restriction: ThreshInhom >= 0.0
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
threshShape
≤
1
Minimum increment: 0.01
Recommended increment: 0.1
Restriction: 0.0 <= ThreshShape && ThreshShape <= 1.0
Used smoothing method.
Default value:
'gauss'
"gauss"
"gauss"
"gauss"
"gauss"
"gauss"
List of values: 'gauss' "gauss" "gauss" "gauss" "gauss" "gauss" , 'mean' "mean" "mean" "mean" "mean" "mean"
Elimination of multiply detected points.
Default value:
'false'
"false"
"false"
"false"
"false"
"false"
List of values: 'false' "false" "false" "false" "false" "false" , 'true' "true" "true" "true" "true" "true"
Row coordinates of the detected junction points.
Column coordinates of the detected junction points.
Row part of the covariance matrix of the detected
junction points.
Mixed part of the covariance matrix of the detected
junction points.
Column part of the covariance matrix of the detected
junction points.
Row coordinates of the detected area points.
Column coordinates of the detected area points.
Row part of the covariance matrix of the detected
area points.
Mixed part of the covariance matrix of the detected
area points.
Column part of the covariance matrix of the detected
area points.
points_foerstner points_foerstner PointsFoerstner points_foerstner PointsFoerstner PointsFoerstner 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>) set_system("no_object_result",<Result>) SetSystem("no_object_result",<Result>) SetSystem("no_object_result",<Result>) . If necessary, an
exception is raised.
gen_cross_contour_xld gen_cross_contour_xld GenCrossContourXld gen_cross_contour_xld GenCrossContourXld GenCrossContourXld ,
disp_cross disp_cross DispCross disp_cross DispCross DispCross
points_harris points_harris PointsHarris points_harris PointsHarris PointsHarris ,
points_lepetit points_lepetit PointsLepetit points_lepetit PointsLepetit PointsLepetit ,
points_harris_binomial points_harris_binomial PointsHarrisBinomial points_harris_binomial PointsHarrisBinomial PointsHarrisBinomial
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.
Foundation