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Find boundaries using parabolic model

uses the random sample consensus (RANSAC) algorithm to find parabolic lane boundary
models that fit a set of boundary points and an approximate width. Each model in the
returned array of `boundaries`

= findParabolicLaneBoundaries(`xyBoundaryPoints`

,`approxBoundaryWidth`

)`parabolicLaneBoundary`

objects
contains the `[A B C]`

coefficients of its second-degree polynomial
equation and the strength of the boundary estimate.

`[`

also returns a cell array of inlier boundary points for each boundary model
found.`boundaries`

,`boundaryPoints`

]
= findParabolicLaneBoundaries(`xyBoundaryPoints`

,`approxBoundaryWidth`

)

`[___] = findParabolicLaneBoundaries(___,`

uses options specified by one or more `Name,Value`

)`Name,Value`

pair arguments,
with any of the preceding syntaxes.

To fit a single boundary model to a double lane marker, set the

`approxBoundaryWidth`

argument to be large enough to include the width spanning both lane markers.

This function uses

`fitPolynomialRANSAC`

(Computer Vision Toolbox) to find parabolic models. Because this algorithm uses random sampling, the output can vary between runs.The

`maxDistance`

parameter of`fitPolynomialRANSAC`

(Computer Vision Toolbox) is set to half the width specified in the`approxBoundaryWidth`

argument. Points are considered inliers if they are within the boundary width. The function obtains the final boundary model using a least-squares fit on the inlier points.

`birdsEyePlot`

| `birdsEyeView`

| `monoCamera`

| `parabolicLaneBoundary`

| `segmentLaneMarkerRidge`

| `fitPolynomialRANSAC`

(Computer Vision Toolbox)