critical_points_sub_pix🔗
Short description🔗
critical_points_sub_pix — Subpixel precise detection of critical points in an image.
Signature🔗
critical_points_sub_pix( image Image, string Filter, real Sigma, real Threshold, out point.y RowMin, out point.x ColumnMin, out point.y RowMax, out point.x ColumnMax, out point.y RowSaddle, out point.x ColumnSaddle )
Description🔗
critical_points_sub_pix extracts critical points, i.e., local
maxima, local minima, and saddle points, from the image Image with
subpixel precision.
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
extremal values and 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 critical point if the absolute values of both
eigenvalues of the Hessian matrix are greater than Threshold. The
eigenvalues correspond to the curvature of the gray
value surface. If both eigenvalues are negative, the point is a local
maximum, if both are positive, a local minimum, and if they have different
signs, a saddle point.
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🔗
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🔗
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: '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: 5.0
Suggested values: 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0
Restriction: Threshold >= 0.0
RowMin (output_control) point.y-array → (real)
Row coordinates of the detected minima.
ColumnMin (output_control) point.x-array → (real)
Column coordinates of the detected minima.
RowMax (output_control) point.y-array → (real)
Row coordinates of the detected maxima.
ColumnMax (output_control) point.x-array → (real)
Column coordinates of the detected maxima.
RowSaddle (output_control) point.y-array → (real)
Row coordinates of the detected saddle points.
ColumnSaddle (output_control) point.x-array → (real)
Column coordinates of the detected saddle points.
Result🔗
critical_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.
Combinations with other operators🔗
Combinations
Possible successors
gen_cross_contour_xld, disp_cross
Alternatives
local_min_sub_pix, local_max_sub_pix, saddle_points_sub_pix
See also
local_min, local_max, plateaus, plateaus_center, lowlands, lowlands_center
Module🔗
Foundation