Operator Reference

HALCON Operator Reference / Segmentation / Topography
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saddle_points_sub_pixT_saddle_points_sub_pixSaddlePointsSubPixSaddlePointsSubPixsaddle_points_sub_pix (Operator)

saddle_points_sub_pixT_saddle_points_sub_pixSaddlePointsSubPixSaddlePointsSubPixsaddle_points_sub_pix — Subpixel precise detection of saddle points in an image.

Signature

saddle_points_sub_pix(Image : : Filter, Sigma, Threshold : Row, Column)

Herror T_saddle_points_sub_pix(const Hobject Image, const Htuple Filter, const Htuple Sigma, const Htuple Threshold, Htuple* Row, Htuple* Column)

void SaddlePointsSubPix(const HObject& Image, const HTuple& Filter, const HTuple& Sigma, const HTuple& Threshold, HTuple* Row, HTuple* Column)

void HImage::SaddlePointsSubPix(const HString& Filter, double Sigma, double Threshold, HTuple* Row, HTuple* Column) const

void HImage::SaddlePointsSubPix(const char* Filter, double Sigma, double Threshold, HTuple* Row, HTuple* Column) const

void HImage::SaddlePointsSubPix(const wchar_t* Filter, double Sigma, double Threshold, HTuple* Row, HTuple* Column) const   ( Windows only)

static void HOperatorSet.SaddlePointsSubPix(HObject image, HTuple filter, HTuple sigma, HTuple threshold, out HTuple row, out HTuple column)

void HImage.SaddlePointsSubPix(string filter, double sigma, double threshold, out HTuple row, out HTuple column)

def saddle_points_sub_pix(image: HObject, filter: str, sigma: float, threshold: float) -> Tuple[Sequence[float], Sequence[float]]

Description

saddle_points_sub_pixsaddle_points_sub_pixSaddlePointsSubPixSaddlePointsSubPixsaddle_points_sub_pix extracts saddle points from the image ImageImageImageimageimage with subpixel precision, i.e., points where along one direction the image intensity is minimal while at the same time along a different direction the image intensity is maximal. To do so, in each point the input image is approximated by a quadratic polynomial in x and y and subsequently the polynomial is examined for saddle points. The partial derivatives, which are necessary for setting up the polynomial, are calculated either with various Gaussian derivatives or using the facet model, depending on FilterFilterFilterfilterfilter. In the first case, SigmaSigmaSigmasigmasigma determines the size of the Gaussian kernels, while in the second case, before being processed the input image is smoothed by a Gaussian whose size is determined by SigmaSigmaSigmasigmasigma. Therefore, 'facet'"facet""facet""facet""facet" results in a faster extraction at the expense of slightly less accurate results. A point is accepted to be a saddle point if the absolute values of both eigenvalues of the Hessian matrix are greater than ThresholdThresholdThresholdthresholdthreshold but their signs differ. The eigenvalues correspond to the curvature of the gray value surface.

saddle_points_sub_pixsaddle_points_sub_pixSaddlePointsSubPixSaddlePointsSubPixsaddle_points_sub_pix is especially useful for the detection of corners, where fields of different intensity join together like the black and white fields of a chess board. Their high contrast and shape facilitate the location and the determination of the precise position of such corners.

Attention

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

  • 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.

Parameters

ImageImageImageimageimage (input_object)  singlechannelimage → objectHImageHObjectHObjectHobject (byte / int1 / int2 / uint2 / int4 / real)

Input image.

FilterFilterFilterfilterfilter (input_control)  string → HTuplestrHTupleHtuple (string) (string) (HString) (char*)

Method for the calculation of the partial derivatives.

Default: 'facet' "facet" "facet" "facet" "facet"

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

SigmaSigmaSigmasigmasigma (input_control)  real → HTuplefloatHTupleHtuple (real) (double) (double) (double)

Sigma of the Gaussian. If FilterFilterFilterfilterfilter is 'facet', SigmaSigmaSigmasigmasigma may be 0.0 to avoid the smoothing of the input image.

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

Restriction: Sigma >= 0.0

ThresholdThresholdThresholdthresholdthreshold (input_control)  real → HTuplefloatHTupleHtuple (real) (double) (double) (double)

Minimum absolute value of the eigenvalues of the Hessian matrix.

Default: 5.0

Suggested values: 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0

Restriction: Threshold >= 0.0

RowRowRowrowrow (output_control)  point.y-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Row coordinates of the detected saddle points.

ColumnColumnColumncolumncolumn (output_control)  point.x-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Column coordinates of the detected saddle points.

Result

saddle_points_sub_pixsaddle_points_sub_pixSaddlePointsSubPixSaddlePointsSubPixsaddle_points_sub_pix 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>)set_system("no_object_result",<Result>). If necessary, an exception is raised.

Possible Successors

gen_cross_contour_xldgen_cross_contour_xldGenCrossContourXldGenCrossContourXldgen_cross_contour_xld, disp_crossdisp_crossDispCrossDispCrossdisp_cross

Alternatives

critical_points_sub_pixcritical_points_sub_pixCriticalPointsSubPixCriticalPointsSubPixcritical_points_sub_pix, local_min_sub_pixlocal_min_sub_pixLocalMinSubPixLocalMinSubPixlocal_min_sub_pix, local_max_sub_pixlocal_max_sub_pixLocalMaxSubPixLocalMaxSubPixlocal_max_sub_pix

Module

Foundation

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