Uses of Class
stitching.model.NotEnoughDataPointsException

Packages that use NotEnoughDataPointsException

Package	Description
stitching.model

Uses of NotEnoughDataPointsException in stitching.model

Methods in stitching.model that throw NotEnoughDataPointsException

Modifier and Type	Method	Description
static final <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.
abstract void	Model.fit(Collection<PointMatch> matches)	Fit the Model to a set of data points minimizing the global transfer error.
final void	TranslationModel2D.fit(Collection<PointMatch> matches)
final void	TranslationModel3D.fit(Collection<PointMatch> matches)
final 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 final <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.