fit_line_contour_xldfit_line_contour_xldFitLineContourXldFitLineContourXldfit_line_contour_xld (Operator)

Name

fit_line_contour_xldfit_line_contour_xldFitLineContourXldFitLineContourXldfit_line_contour_xld — Approximate XLD contours by line segments.

Signature

Description

fit_line_contour_xldfit_line_contour_xldFitLineContourXldFitLineContourXldFitLineContourXldfit_line_contour_xld approximates the XLD contours ContoursContoursContoursContourscontourscontours by line segments. It does not perform a segmentation of the input contours. Thus, one has to make sure that each contour corresponds to one and only one line segment. The operator returns for each contour the start point (RowBeginRowBeginRowBeginRowBeginrowBeginrow_begin, ColBeginColBeginColBeginColBegincolBegincol_begin), the end point (RowEndRowEndRowEndRowEndrowEndrow_end, ColEndColEndColEndColEndcolEndcol_end), and the regression line to the contour given by the normal vector (NrNrNrNrnrnr, NcNcNcNcncnc) of the line and its distance DistDistDistDistdistdist from the origin, i.e., the line equation is given by .

The algorithm used for the fitting of lines can be selected via AlgorithmAlgorithmAlgorithmAlgorithmalgorithmalgorithm:

'regression'"regression""regression""regression""regression""regression":

Standard 'least squares' line fitting.

'huber'"huber""huber""huber""huber""huber":

Weighted 'least squares' line fitting, where the impact of outliers is decreased based on the approach of Huber (see below).

'tukey'"tukey""tukey""tukey""tukey""tukey":

Weighted 'least squares' line fitting, where outliers are ignored based on the approach of Tukey (see below).

'drop'"drop""drop""drop""drop""drop":

'least squares' line fitting, where outliers are ignored. In particular, all contour points further away from the contour than the mean distance to the regression line multiplied with the ClippingFactorClippingFactorClippingFactorClippingFactorclippingFactorclipping_factor (see below) are ignored for the calculation of the undistorted regression line.

'gauss'"gauss""gauss""gauss""gauss""gauss":

Weighted 'least squares' line fitting, where the impact of outliers is decreased based on the mean value and the standard deviation of the distances of all contour points from the approximating line.

For 'huber'"huber""huber""huber""huber""huber", 'tukey'"tukey""tukey""tukey""tukey""tukey", and 'drop'"drop""drop""drop""drop""drop" a robust error statistics is used to estimate the standard deviation of the distances from the contour points without outliers from the approximating line. The parameter ClippingFactorClippingFactorClippingFactorClippingFactorclippingFactorclipping_factor (a scaling factor for the standard deviation) controls the amount of outliers: The smaller the value chosen for ClippingFactorClippingFactorClippingFactorClippingFactorclippingFactorclipping_factor the more outliers are detected. The detection of outliers is repeated. The parameter IterationsIterationsIterationsIterationsiterationsiterations specifies the number of iterations. In the mode 'regression'"regression""regression""regression""regression""regression" this value is ignored. Note that in the approach of Tukey ('tukey'"tukey""tukey""tukey""tukey""tukey"), the outliers are removed before performing the approximation and all other points are weighted, whereas in the approach of Huber ('huber'"huber""huber""huber""huber""huber"), the outliers still have a small influence. Particularly, for outliers the optimization is influenced linearly and for points with a smaller distance it is influenced to the square. In practice, the approach of Tukey is recommended.

To reduce the computational load, the fitting of lines can be restricted to a subset of the contour points: If a value other than -1 is assigned to MaxNumPointsMaxNumPointsMaxNumPointsMaxNumPointsmaxNumPointsmax_num_points, only up to MaxNumPointsMaxNumPointsMaxNumPointsMaxNumPointsmaxNumPointsmax_num_points points - uniformly distributed over the contour - are used.

The start point and the end point of a line segment is determined by projecting the first and the last point of the corresponding contour to the approximating line. Due to artifacts in the pre-processing the start and end points of a contour might be faulty. Therefore, it is possible to exclude ClippingEndPointsClippingEndPointsClippingEndPointsClippingEndPointsclippingEndPointsclipping_end_points points at the beginning and at the end of a contour from the line fitting. However, they are still used for the determination of the start point and the end point of the line segment.

The minimum necessary number of contour points for fitting a line is two. Therefore, it is required that the number of contour points is at least .

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

read_image (Image, 'mreut')
edges_sub_pix (Image, Edges, 'lanser2', 0.5, 20, 40)
gen_polygons_xld (Edges, Polygons, 'ramer', 2)
split_contours_xld (Polygons, Contours, 'polygon', 1, 5)
fit_line_contour_xld (Contours, 'regression', -1, 0, 5, 2, RowBegin, \
                      ColBegin, RowEnd, ColEnd, Nr, Nc, Dist)

read_image (Image, 'mreut')
edges_sub_pix (Image, Edges, 'lanser2', 0.5, 20, 40)
gen_polygons_xld (Edges, Polygons, 'ramer', 2)
split_contours_xld (Polygons, Contours, 'polygon', 1, 5)
fit_line_contour_xld (Contours, 'regression', -1, 0, 5, 2, RowBegin, \
                      ColBegin, RowEnd, ColEnd, Nr, Nc, Dist)

read_image (Image, 'mreut')
edges_sub_pix (Image, Edges, 'lanser2', 0.5, 20, 40)
gen_polygons_xld (Edges, Polygons, 'ramer', 2)
split_contours_xld (Polygons, Contours, 'polygon', 1, 5)
fit_line_contour_xld (Contours, 'regression', -1, 0, 5, 2, RowBegin, \
                      ColBegin, RowEnd, ColEnd, Nr, Nc, Dist)

read_image (Image, 'mreut')
edges_sub_pix (Image, Edges, 'lanser2', 0.5, 20, 40)
gen_polygons_xld (Edges, Polygons, 'ramer', 2)
split_contours_xld (Polygons, Contours, 'polygon', 1, 5)
fit_line_contour_xld (Contours, 'regression', -1, 0, 5, 2, RowBegin, \
                      ColBegin, RowEnd, ColEnd, Nr, Nc, Dist)

read_image (Image, 'mreut')
edges_sub_pix (Image, Edges, 'lanser2', 0.5, 20, 40)
gen_polygons_xld (Edges, Polygons, 'ramer', 2)
split_contours_xld (Polygons, Contours, 'polygon', 1, 5)
fit_line_contour_xld (Contours, 'regression', -1, 0, 5, 2, RowBegin, \
                      ColBegin, RowEnd, ColEnd, Nr, Nc, Dist)

Result

fit_line_contour_xldfit_line_contour_xldFitLineContourXldFitLineContourXldFitLineContourXldfit_line_contour_xld returns 2 ( H_MSG_TRUE) if all parameter values are correct, and line segments could be fitted to the input contours. 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>)SetSystem("no_object_result",<Result>)set_system("no_object_result",<Result>). If necessary, an exception is raised. If the parameter ClippingFactorClippingFactorClippingFactorClippingFactorclippingFactorclipping_factor is chosen too small, i.e., all points are classified as outliers, the error 3264 is raised.

Possible Predecessors

gen_contours_skeleton_xldgen_contours_skeleton_xldGenContoursSkeletonXldGenContoursSkeletonXldGenContoursSkeletonXldgen_contours_skeleton_xld, lines_gausslines_gaussLinesGaussLinesGaussLinesGausslines_gauss, lines_facetlines_facetLinesFacetLinesFacetLinesFacetlines_facet, edges_sub_pixedges_sub_pixEdgesSubPixEdgesSubPixEdgesSubPixedges_sub_pix, smooth_contours_xldsmooth_contours_xldSmoothContoursXldSmoothContoursXldSmoothContoursXldsmooth_contours_xld

Possible Successors

disp_linedisp_lineDispLineDispLineDispLinedisp_line, line_orientationline_orientationLineOrientationLineOrientationLineOrientationline_orientation

See also

regress_contours_xldregress_contours_xldRegressContoursXldRegressContoursXldRegressContoursXldregress_contours_xld, get_regress_params_xldget_regress_params_xldGetRegressParamsXldGetRegressParamsXldGetRegressParamsXldget_regress_params_xld

Module

Foundation