close_edges_lengthclose_edges_lengthCloseEdgesLengthCloseEdgesLengthclose_edges_length (Operator)

Name

close_edges_lengthclose_edges_lengthCloseEdgesLengthCloseEdgesLengthclose_edges_length — Close edge gaps using the edge amplitude image.

Signature

close_edges_length(Edges, Gradient : ClosedEdges : MinAmplitude, MaxGapLength : )

Description

close_edges_lengthclose_edges_lengthCloseEdgesLengthCloseEdgesLengthclose_edges_length closes gaps in the output of an edge detector, and thus tries to produce complete object contours. This operator expects as input the edges (EdgesEdgesEdgesedgesedges) and amplitude image (GradientGradientGradientgradientgradient) returned by typical edge operators, such as edges_imageedges_imageEdgesImageEdgesImageedges_image or sobel_ampsobel_ampSobelAmpSobelAmpsobel_amp.

Contours are closed in two steps: First, one pixel wide gaps in the input contours are closed, and isolated points are eliminated. After this, open contours are extended by up to MaxGapLengthMaxGapLengthMaxGapLengthmaxGapLengthmax_gap_length points by adding edge points until either the contour is closed or no more significant edge points can be found. A gradient is regarded as significant if it is larger than MinAmplitudeMinAmplitudeMinAmplitudeminAmplitudemin_amplitude. The neighboring points examined as possible new edge points are the point in the direction of the contour and its two adjacent points in an 8-neighborhood. For each of these points, the sum of its gradient and the maximum gradient of that points three possible neighbors is calculated (look ahead of length 1). The point with the maximum sum is then chosen as the new edge 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

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Automatically parallelized on tuple level.

Parameters

EdgesEdgesEdgesedgesedges (input_object) region(-array) → object

Region containing one pixel thick edges.

GradientGradientGradientgradientgradient (input_object) singlechannelimage → object (byte / uint2)

Edge amplitude (gradient) image.

ClosedEdgesClosedEdgesClosedEdgesclosedEdgesclosed_edges (output_object) region(-array) → object

Region containing closed edges.

MinAmplitudeMinAmplitudeMinAmplitudeminAmplitudemin_amplitude (input_control) integer → (integer)

Minimum edge amplitude.

Default: 16

Suggested values: 5, 8, 10, 12, 16, 20, 25, 30, 40, 50

Value range: 0 ≤ MinAmplitude MinAmplitude MinAmplitude minAmplitude min_amplitude ≤ 255

Minimum increment: 1

Recommended increment: 1

MaxGapLengthMaxGapLengthMaxGapLengthmaxGapLengthmax_gap_length (input_control) integer → (integer)

Maximal number of points by which edges are extended.

Default: 3

Suggested values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 30, 40, 50, 70, 100

Value range: 1 ≤ MaxGapLength MaxGapLength MaxGapLength maxGapLength max_gap_length ≤ 127

Minimum increment: 1

Recommended increment: 1

Example (C)

sobel_amp(Image,&EdgeAmp,"sum_abs",5);
threshold(EdgeAmp,&EdgeRegion,40.0,255.0);
skeleton(EdgeRegion,&ThinEdge);
close_edges_length(ThinEdge,EdgeAmp,&CloseEdges,15,3);

Result

close_edges_lengthclose_edges_lengthCloseEdgesLengthCloseEdgesLengthclose_edges_length returns 2 ( H_MSG_TRUE) if all parameters are correct. 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 Predecessors

edges_imageedges_imageEdgesImageEdgesImageedges_image, sobel_ampsobel_ampSobelAmpSobelAmpsobel_amp, thresholdthresholdThresholdThresholdthreshold, skeletonskeletonSkeletonSkeletonskeleton

Alternatives

close_edgesclose_edgesCloseEdgesCloseEdgesclose_edges, dilation1dilation1Dilation1Dilation1dilation1, closingclosingClosingClosingclosing

References

M. Üsbeck: “Untersuchungen zur echtzeitfähigen Segmentierung”; Studienarbeit, Bayerisches Forschungszentrum für Wissensbasierte Systeme (FORWISS), Erlangen, 1993.

Module

Foundation

Operators