saddle_points_sub_pix — Subpixel precise detection of saddle points in an image.
saddle_points_sub_pix extracts saddle points from the image
Image 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 Filter.
In the first case, Sigma 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 Sigma.
Therefore, '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 Threshold but
their signs differ. The eigenvalues correspond to the curvature of the gray
value surface.
saddle_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.
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.
Image (input_object) singlechannelimage → object (byte / int1 / int2 / uint2 / int4 / real)
Input image.
Filter (input_control) string → (string)
Method for the calculation of the partial derivatives.
Default value: 'facet'
List of values: 'facet', 'gauss'
Sigma (input_control) real → (real)
Sigma of the Gaussian.
If Filter is 'facet', Sigma 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
Threshold (input_control) real → (real)
Minimum absolute value of the eigenvalues of the Hessian matrix.
Default value: 5.0
Suggested values: 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0
Restriction: Threshold >= 0.0
Row (output_control) point.y-array → (real)
Row coordinates of the detected saddle points.
Column (output_control) point.x-array → (real)
Column coordinates of the detected saddle points.
saddle_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>). If
necessary, an exception is raised.
gen_cross_contour_xld,
disp_cross
critical_points_sub_pix,
local_min_sub_pix,
local_max_sub_pix
Foundation