critical_points_sub_pix
— Subpixel precise detection of critical points in an image.
critical_points_sub_pix(Image : : Filter, Sigma, Threshold : RowMin, ColumnMin, RowMax, ColumnMax, RowSaddle, ColumnSaddle)
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.
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
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.
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.
gen_cross_contour_xld
,
disp_cross
local_min_sub_pix
,
local_max_sub_pix
,
saddle_points_sub_pix
local_min
,
local_max
,
plateaus
,
plateaus_center
,
lowlands
,
lowlands_center
Foundation