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FitSegmentToStripe

Performs a series of 1D stripe detections and finds a segment that best matches the detected points.

Applications: Precise detection of a straight stripe, whose rough location is known beforehand.

Syntax

C++
C#

void avl::FitSegmentToStripe
(
const avl::Image& inImage,
const avl::SegmentFittingMap& inFittingMap,
const StripeScanParams& inStripeScanParams,
avl::Selection::Type inStripeSelection,
atl::Optional<const avl::LocalBlindness&> inLocalBlindness,
float inMaxIncompleteness,
atl::Optional<avl::LineMEstimator::Type> inOutlierSuppression,
atl::Conditional<avl::Segment2D>& outSegment,
atl::Conditional<avl::Segment2D>& outLeftSegment,
atl::Conditional<avl::Segment2D>& outRightSegment,
atl::Optional<atl::Array<atl::Conditional<avl::Stripe1D>>&> outStripes = atl::NIL,
atl::Optional<atl::Array<avl::Point2D>&> outStripePoints = atl::NIL,
atl::Optional<atl::Conditional<avl::Profile>&> outDeviationProfile = atl::NIL,
atl::Array<avl::Profile>& diagBrightnessProfiles,
atl::Array<avl::Profile>& diagResponseProfiles
)


Parameters

Name Type Range Default Description
inImage const Image& Image to fit segment to
inFittingMap const SegmentFittingMap& Input fitting map
inStripeScanParams const StripeScanParams& Parameters controlling the stripe extraction process
inStripeSelection Selection::Type Selection::​Best Selection mode of stripe
inLocalBlindness Optional<const LocalBlindness&> NIL Defines conditions in which weaker edges can be detected in the vicinity of stronger edges
inMaxIncompleteness float 0.0 - 0.999 0.1f Maximal fraction of stripe points not found
inOutlierSuppression Optional<LineMEstimator::Type> NIL Selects a method for ignoring incorrectly detected points
outSegment Conditional<Segment2D>& Fitted segment in the middle of found stripe
outLeftSegment Conditional<Segment2D>& Fitted left segment
outRightSegment Conditional<Segment2D>& Fitted right segment
outStripes Optional<Array<Conditional<Stripe1D>>&> NIL Found stripes
outStripePoints Optional<Array<Point2D>&> NIL Extracted points of middle segment of an image stripe
outDeviationProfile Optional<Conditional<Profile>&> NIL Profile of distances between the actual segment points and the corresponding reference segment points
diagBrightnessProfiles Array<Profile>& Extracted image profiles
diagResponseProfiles Array<Profile>& Profiles of the edge (derivative) operator response

Optional Outputs

The computation of following outputs can be switched off by passing value atl::NIL to these parameters: outStripes, outStripePoints, outDeviationProfile.

Description

The operation tries to fit a given segment to stripe present in the inImage image. Internally, it performs a series of scans with the ScanSingleStripe filter using inFittingMap previously generated from the object being fitted. The found points are then used to determine the actual position of the segment in the image. Only inMaxIncompleteness fraction of these scans may fail. If the fitting according to the given parameters is not possible, outSegment is set to Nil.

Hints

• Connect an input image to the inImage input.
• Define inStripeScanParams.StripePolarity to detect a particular stripe type, and only that type.
• Adjust inStripeScanParams.MinStripeWidth and inStripeScanParams.MaxStripeWidth to the expected thickness of the stripe (in pixels).
• If no or too few stripe points are found, try decreasing inStripeScanParams.MinMagnitude.
• If some of the scans may fail, set the inMaxIncompleteness input accordingly.
• If some of the scans may produce false results, try different values of the inOutlierSuppression input.
• Use the outStripePoints outputs to visualize the scanning results.

Examples

Fitting a segment to the dark stripe of a nail
(inStripeScanParams.Polarity = Dark).

Remarks

Read more about Local Coordinate Systems in Machine Vision Guide: Local Coordinate Systems.

This filter is a part of the Shape Fitting toolset. To read more about this technique, one can refer to the Shape Fitting chapter of our Machine Vision Guide

Hardware Acceleration

This operation supports automatic parallelization for multicore and multiprocessor systems.