Uses of Class stitching.model.NotEnoughDataPointsException (Fiji Javadocs 0-SNAPSHOT API)

Packages that use NotEnoughDataPointsException
Package Description

stitching.model

Packages that use NotEnoughDataPointsException
Package	Description
stitching.model

Methods in stitching.model that throw NotEnoughDataPointsException
Modifier and Type	Method and Description
`static <M extends Model> M`	Model.`filter(Class<M> modelClass, Collection<PointMatch> candidates, Collection<PointMatch> inliers)` Estimate a `Model` and filter potential outliers by robust iterative regression.
`static <M extends Model> M`	Model.`filterRansac(Class<M> modelType, List<PointMatch> candidates, Collection<PointMatch> inliers, int iterations, float max_epsilon, float min_inlier_ratio)` Estimate the best model for a set of feature correspondence candidates.
`void`	TranslationModel3D.`fit(Collection<PointMatch> matches)`
`void`	TranslationModel2D.`fit(Collection<PointMatch> matches)`
`abstract void`	Model.`fit(Collection<PointMatch> matches)` Fit the `Model` to a set of data points minimizing the global transfer error.
`void`	Tile.`fitModel()` Update the transformation `Model`.
`void`	TileConfiguration.`optimize(float maxAllowedError, int maxIterations, int maxPlateauwidth)` Minimize the displacement of all correspondence pairs of all tiles.
`static <M extends Model> M`	Model.`ransac(Class<M> modelClass, List<PointMatch> candidates, Collection<PointMatch> inliers, int iterations, double epsilon, double min_inlier_ratio)` Estimate a `Model` from a set with many outliers by first filtering the worst outliers with `ransac` \citet[{FischlerB81} tand filter potential outliers by robust iterative regression.

Uses of Classstitching.model.NotEnoughDataPointsException