Uses of Class mpicbg.models.NotEnoughDataPointsException (Fiji Javadocs 0-SNAPSHOT API)

Packages that use NotEnoughDataPointsException
Package	Description
<Unnamed>
bdv.viewer.animate
bunwarpj.trakem2.transform
lenscorrection
mpicbg.icp
mpicbg.ij
mpicbg.ij.plugin
mpicbg.models
mpicbg.pointdescriptor.model
mpicbg.spim.mpicbg
org.janelia.thickness.inference
process

Uses of NotEnoughDataPointsException in <Unnamed>

Methods in <Unnamed> that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`protected void`	Transform_MovingLeastSquaresMesh.`updateMapping()`
`protected void`	Transform_ThinPlateSpline.`updateMapping()`

Uses of NotEnoughDataPointsException in bdv.viewer.animate

Methods in bdv.viewer.animate that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`<P extends PointMatch> void`	SimilarityModel3D.`fit(Collection<P> matches)`
`void`	SimilarityModel3D.`fit(double[][] p, double[][] q, double[] w)`
`void`	SimilarityModel3D.`fit(float[][] p, float[][] q, float[] w)`

Uses of NotEnoughDataPointsException in bunwarpj.trakem2.transform

Methods in bunwarpj.trakem2.transform that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`<P extends PointMatch> void`	CubicBSplineTransform.`fit(Collection<P> matches)`

Uses of NotEnoughDataPointsException in lenscorrection

Methods in lenscorrection that throw NotEnoughDataPointsException
Modifier and Type Method and Description

<P extends PointMatch> void PolynomialModel2D.fit(Collection<P> pointMatches)

Methods in lenscorrection that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`<P extends PointMatch> void`	PolynomialModel2D.`fit(Collection<P> pointMatches)`

Uses of NotEnoughDataPointsException in mpicbg.icp

Methods in mpicbg.icp that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`void`	ICP.`estimateIntialModel(List<PointMatch> matches, Model<?> model)` Estimates an initial `Model` based on some given `PointMatch`es.
`void`	ICP.`runICPIteration(Model<?> lastModel, Model<?> newModel)` Performs one iteration of the `ICP`.

Uses of NotEnoughDataPointsException in mpicbg.ij

Methods in mpicbg.ij that throw NotEnoughDataPointsException
Modifier and Type Method and Description

protected abstract void InteractiveMapping.updateMapping()

Methods in mpicbg.ij that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`protected abstract void`	InteractiveMapping.`updateMapping()`

Uses of NotEnoughDataPointsException in mpicbg.ij.plugin

Methods in mpicbg.ij.plugin that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`static <M extends AbstractModel<M> & InverseCoordinateTransform> ij.ImagePlus`	AlignStacksWithLandmarks.`createAlignedStack(ij.ImagePlus source, ij.ImagePlus target, Collection<PointMatch> pointMatches, M model)`

Uses of NotEnoughDataPointsException in mpicbg.models

Methods in mpicbg.models that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`<P extends PointMatch> boolean`	Model.`filter(Collection<P> candidates, Collection<P> inliers)` Call `Model.filter(Collection, Collection, double)` with maxTrust = 4 and minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	AbstractModel.`filter(Collection<P> candidates, Collection<P> inliers)` Call `AbstractModel.filter(Collection, Collection, double)` with maxTrust = 4 and minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	Model.`filter(Collection<P> candidates, Collection<P> inliers, double maxTrust)` Call `Model.filter(Collection, Collection, double, int)` with minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	AbstractModel.`filter(Collection<P> candidates, Collection<P> inliers, double maxTrust)` Call `AbstractModel.filter(Collection, Collection, double, int)` with minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	Model.`filter(Collection<P> candidates, Collection<P> inliers, double maxTrust, int minNumInliers)` Estimate the `Model` and filter potential outliers by robust iterative regression.
`<P extends PointMatch> boolean`	AbstractModel.`filter(Collection<P> candidates, Collection<P> inliers, double maxTrust, int minNumInliers)` Estimate the `AbstractModel` and filter potential outliers by robust iterative regression.
`<P extends PointMatch> boolean`	Model.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio)` Call `Model.filterRansac(List, Collection, int, double, double, double)` with maxTrust = 4.
`<P extends PointMatch> boolean`	AbstractModel.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio)` Call `AbstractModel.filterRansac(List, Collection, int, double, double, double)` with maxTrust = 4.
`<P extends PointMatch> boolean`	Model.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio, double maxTrust)` Call `Model.filterRansac(List, Collection, int, double, double, int, double)` with minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	AbstractModel.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio, double maxTrust)` Call `AbstractModel.filterRansac(List, Collection, int, double, double, int, double)` with minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	Model.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio, int minNumInliers)` Call `Model.filterRansac(List, Collection, int, double, double, int, double)` with maxTrust = 4.
`<P extends PointMatch> boolean`	AbstractModel.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio, int minNumInliers)` Call `AbstractModel.filterRansac(List, Collection, int, double, double, int, double)` with maxTrust = 4.
`<P extends PointMatch> boolean`	Model.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio, int minNumInliers, double maxTrust)` Estimate a `Model` from a set with many outliers by first filtering the worst outliers with `RANSAC` \citet[{FischlerB81} and filter potential outliers by robust iterative regression.
`<P extends PointMatch> boolean`	AbstractModel.`filterRansac(List<P> candidates, Collection<P> inliers, int iterations, double maxEpsilon, double minInlierRatio, int minNumInliers, double maxTrust)` Estimate a `AbstractModel` from a set with many outliers by first filtering the worst outliers with `RANSAC` \citet[{FischlerB81} and filter potential outliers by robust iterative regression.
`<P extends PointMatch> void`	TranslationModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	TranslationModel2D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	TranslationModel1D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	SimilarityModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	SimilarityModel2D.`fit(Collection<P> matches)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`<P extends PointMatch> void`	RigidModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	RigidModel2D.`fit(Collection<P> matches)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`<P extends PointMatch> void`	Model.`fit(Collection<P> matches)` Fit the `Model` to a set of data points minimizing the global transfer error.
`<P extends PointMatch> void`	InterpolatedModel.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	InterpolatedAffineModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	InterpolatedAffineModel2D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	InterpolatedAffineModel1D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	HomographyModel2D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	AffineModel3D.`fit(Collection<P> matches)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`<P extends PointMatch> void`	AffineModel2D.`fit(Collection<P> matches)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`<P extends PointMatch> void`	AffineModel1D.`fit(Collection<P> matches)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	TranslationModel2D.`fit(double[][] p, double[][] q, double[] w)`
`void`	TranslationModel1D.`fit(double[][] p, double[][] q, double[] w)`
`void`	SimilarityModel3D.`fit(double[][] p, double[][] q, double[] w)`
`void`	SimilarityModel2D.`fit(double[][] p, double[][] q, double[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`void`	RigidModel2D.`fit(double[][] p, double[][] q, double[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`void`	Model.`fit(double[][] p, double[][] q, double[] w)` Fit the `Model` to a set of data points minimizing the global transfer error.
`void`	AffineModel3D.`fit(double[][] p, double[][] q, double[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	AffineModel2D.`fit(double[][] p, double[][] q, double[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	AffineModel1D.`fit(double[][] p, double[][] q, double[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	AbstractModel.`fit(double[][] p, double[][] q, double[] w)` Default fit implementation using `Model.fit(Collection)`.
`void`	TranslationModel2D.`fit(float[][] p, float[][] q, float[] w)`
`void`	TranslationModel1D.`fit(float[][] p, float[][] q, float[] w)`
`void`	SimilarityModel3D.`fit(float[][] p, float[][] q, float[] w)`
`void`	SimilarityModel2D.`fit(float[][] p, float[][] q, float[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`void`	RigidModel2D.`fit(float[][] p, float[][] q, float[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`void`	Model.`fit(float[][] p, float[][] q, float[] w)` Fit the `Model` to a set of data points minimizing the global transfer error.
`void`	AffineModel3D.`fit(float[][] p, float[][] q, float[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	AffineModel2D.`fit(float[][] p, float[][] q, float[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	AffineModel1D.`fit(float[][] p, float[][] q, float[] w)` Closed form weighted least squares solution as described by \citet{SchaeferAl06}.
`void`	AbstractModel.`fit(float[][] p, float[][] q, float[] w)` Default fit implementation using `Model.fit(Collection)`.
`void`	Tile.`fitModel()` Update the transformation `AbstractModel`.
`<P extends PointMatch> boolean`	Model.`localSmoothnessFilter(Collection<P> candidates, Collection<P> inliers, double sigma, double maxEpsilon, double maxTrust)` Filter a `Collection` of `PointMatches` by the smoothness of their support for the given `Model`.
`void`	TileConfiguration.`optimize(double maxAllowedError, int maxIterations, int maxPlateauwidth)` Minimize the displacement of all `Correspondence pairs` of all `Tiles` and tell about it.
`void`	SpringMesh.`optimize(double maxError, int maxIterations, int maxPlateauwidth)` Optimize the mesh.
`void`	ElasticMovingLeastSquaresMesh.`optimize(double maxError, int maxIterations, int maxPlateauwidth)` Minimize the displacement of all PointMatches of all tiles.
`void`	TileConfiguration.`optimize(double maxAllowedError, int maxIterations, int maxPlateauwidth, double damp)` Minimize the displacement of all `Correspondence pairs` of all `Tiles` and tell about it.
`void`	TileConfiguration.`optimize(ErrorStatistic observer, double maxAllowedError, int maxIterations, int maxPlateauwidth, double damp)` Minimize the displacement of all `Correspondence pairs` of all `Tiles` and tell about it.
`void`	TileConfiguration.`optimizeAndFilter(double maxAllowedError, int maxIterations, int maxPlateauwidth, double maxMeanFactor)`
`void`	TileConfiguration.`optimizeAndFilter(double maxAllowedError, int maxIterations, int maxPlateauwidth, double damp, double maxMeanFactor)`
`void`	ElasticMovingLeastSquaresMesh.`optimizeByStrength(double maxError, int maxIterations, int maxPlateauwidth, ij.process.ByteProcessor ipPlot, ij.ImagePlus impPlot)` Minimize the displacement of all PointMatches of all tiles.
`void`	ElasticMovingLeastSquaresMesh.`optimizeIteration()` Performs one optimization iteration.
`void`	ElasticMovingLeastSquaresMesh.`optimizeIteration(ErrorStatistic observer)` Performs one optimization iteration and writes its error into the ErrorStatistics
`static void`	SpringMesh.`optimizeMeshes(Collection<SpringMesh> meshes, double maxError, int maxIterations, int maxPlateauwidth)` Optimize a `Collection` of connected `SpringMeshes`.
`static void`	SpringMesh.`optimizeMeshes(Collection<SpringMesh> meshes, double maxError, int maxIterations, int maxPlateauwidth, boolean visualize)` Optimize a `Collection` of connected `SpringMeshes`.
`static void`	SpringMesh.`optimizeMeshes(Collection<SpringMesh> meshes, double maxError, int maxIterations, int maxPlateauwidth, double maxStepSize, boolean visualize)` Optimize a `Collection` of connected `SpringMeshes`.
`static void`	SpringMesh.`optimizeMeshes2(Collection<SpringMesh> meshes, double maxError, int maxIterations, int maxPlateauwidth)` Deprecated. Remains for reproducing legacy results
`static void`	SpringMesh.`optimizeMeshes2(Collection<SpringMesh> meshes, double maxError, int maxIterations, int maxPlateauwidth, boolean visualize)` Deprecated. Remains for reproducing legacy results
`void`	TileConfiguration.`optimizeSilently(ErrorStatistic observer, double maxAllowedError, int maxIterations, int maxPlateauwidth)` Minimize the displacement of all `Correspondence pairs` of all `Tiles`
`void`	TileConfiguration.`optimizeSilently(ErrorStatistic observer, double maxAllowedError, int maxIterations, int maxPlateauwidth, double damp)` Minimize the displacement of all `Correspondence pairs` of all `Tiles`
`void`	TileConfiguration.`optimizeSilentlyConcurrent(ErrorStatistic observer, double maxAllowedError, int maxIterations, int maxPlateauwidth, double damp)`
`protected void`	SpringMesh.`optimizeStep(ErrorStatistic observer)` Performs one optimization step.
`List<Tile<?>>`	TileConfiguration.`preAlign()` Computes a pre-alignemnt of all non-fixed `Tile`s by propagating the pairwise models.
`<P extends PointMatch> boolean`	Model.`ransac(List<P> candidates, Collection<P> inliers, int iterations, double epsilon, double minInlierRatio)` Call `Model.ransac(List, Collection, int, double, double, int)` with minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	AbstractModel.`ransac(List<P> candidates, Collection<P> inliers, int iterations, double epsilon, double minInlierRatio)` Call `AbstractModel.ransac(List, Collection, int, double, double, int)` with minNumInliers = `Model.getMinNumMatches()`.
`<P extends PointMatch> boolean`	Model.`ransac(List<P> candidates, Collection<P> inliers, int iterations, double epsilon, double minInlierRatio, int minNumInliers)` Find the `Model` of a set of `PointMatch` candidates containing a high number of outliers using `RANSAC` \citet[{FischlerB81}.
`<P extends PointMatch> boolean`	AbstractModel.`ransac(List<P> candidates, Collection<P> inliers, int iterations, double epsilon, double minInlierRatio, int minNumInliers)` Find the `AbstractModel` of a set of `PointMatch` candidates containing a high number of outliers using `RANSAC` \citet[{FischlerB81}.
`void`	MovingLeastSquaresTransform2.`setMatches(Collection<PointMatch> matches)` Set the control points.
`void`	MovingLeastSquaresTransform.`setMatches(Collection<PointMatch> matches)`
`abstract void`	AbstractMovingLeastSquaresTransform.`setMatches(Collection<PointMatch> matches)`
`void`	MovingLeastSquaresTransform2.`setMatches(float[][] p, float[][] q, float[] w)` Set the control points passing them as arrays that are used by reference.
`void`	MovingLeastSquaresMesh.`updateModels()` Updates each vertex' transformation model by means of moving least squares.

Uses of NotEnoughDataPointsException in mpicbg.pointdescriptor.model

Methods in mpicbg.pointdescriptor.model that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`<P extends PointMatch> void`	TranslationInvariantSimilarityModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	TranslationInvariantRigidModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	TranslationInvariantRigidModel2D.`fit(Collection<P> matches)` Closed form weighted least squares solution as described by \citet{SchaeferAl06} and implemented by Johannes Schindelin.
`<P extends PointMatch> void`	TranslationInvariantFixedModel.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	TranslationInvariantAffineModel3D.`fit(Collection<P> matches)`
`<P extends PointMatch> void`	FixedModel.`fit(Collection<P> matches)`

Uses of NotEnoughDataPointsException in mpicbg.spim.mpicbg

Methods in mpicbg.spim.mpicbg that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`void`	TileConfigurationSPIM.`optimize(double maxAllowedError, int maxIterations, int maxPlateauwidth, int debugLevel)` Minimize the displacement of all `Correspondence pairs` of all `Tiles`
`void`	TileConfigurationSPIM.`optimizeWithSketchTikZ(double maxAllowedError, int maxIterations, int maxPlateauwidth, int debugLevel)` Minimize the displacement of all `Correspondence pairs` of all `Tiles`
`void`	TileConfigurationSPIM.`optimizeWithSketchTikZNuclei(double maxAllowedError, int maxIterations, int maxPlateauwidth, int debugLevel)` Minimize the displacement of all `Correspondence pairs` of all `Tiles`
`List<Tile<?>>`	TileConfigurationSPIM.`preAlign()` Computes a pre-alignemnt of all non-fixed `Tile`s by propagating the pairwise models.

Uses of NotEnoughDataPointsException in org.janelia.thickness.inference

Methods in org.janelia.thickness.inference that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`<T extends RealType<T>,W extends RealType<W>> double[]`	InferFromMatrix.`getMediatedShifts(RandomAccessibleInterval<T> matrix, RandomAccessibleInterval<T> scaledMatrix, double[] lut, double[] scalingFactors, int iteration, RandomAccessibleInterval<double[]>[] correlationFitsStore, double[] shiftsArray, double[] weightSums, RandomAccessibleInterval<W> estimateWeightMatrix, double[] shiftWeights, Options options)`
`void`	InferFromMatrix.ModelRegularization.`regularize(double[] coordinates, Options options)`

Uses of NotEnoughDataPointsException in process

Methods in process that throw NotEnoughDataPointsException
Modifier and Type Method and Description

static void Matching.main(String[] args)

Uses of Classmpicbg.models.NotEnoughDataPointsException

Uses of NotEnoughDataPointsException in <Unnamed>

Uses of NotEnoughDataPointsException in bdv.viewer.animate

Uses of NotEnoughDataPointsException in bunwarpj.trakem2.transform

Uses of NotEnoughDataPointsException in lenscorrection

Uses of NotEnoughDataPointsException in mpicbg.icp

Uses of NotEnoughDataPointsException in mpicbg.ij

Uses of NotEnoughDataPointsException in mpicbg.ij.plugin

Uses of NotEnoughDataPointsException in mpicbg.models

Uses of NotEnoughDataPointsException in mpicbg.pointdescriptor.model

Uses of NotEnoughDataPointsException in mpicbg.spim.mpicbg

Uses of NotEnoughDataPointsException in org.janelia.thickness.inference

Uses of NotEnoughDataPointsException in process

Uses of Class
mpicbg.models.NotEnoughDataPointsException