Uses of Class
stitching.model.PointMatch

Packages that use PointMatch

Package	Description
stitching.model

Uses of PointMatch in stitching.model

Methods in stitching.model that return types with arguments of type PointMatch

Modifier and Type	Method	Description
static final ArrayList<PointMatch>	PointMatch.flip(Collection<PointMatch> matches)	Flip symmetrically, weights remains unchanged.
final ArrayList<PointMatch>	Tile.getMatches()

Methods in stitching.model with parameters of type PointMatch

Modifier and Type	Method	Description
final boolean	Tile.addMatch(PointMatch match)	Add one match.

Method parameters in stitching.model with type arguments of type PointMatch

Modifier and Type	Method	Description
final boolean	Tile.addMatches(Collection<PointMatch> more)	Add more PointMatches.
final void	Tile.connect(Tile o, Collection<PointMatch> matches)	connect two tiles by a set of point correspondences re-weighs the point correpondences We set a weigh of 1.0 / num_matches to each correspondence to equalize the connections between tiles during minimization.
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.
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)
static final ArrayList<PointMatch>	PointMatch.flip(Collection<PointMatch> matches)	Flip symmetrically, weights remains unchanged.
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.
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.
abstract void	Model.shake(Collection<PointMatch> matches, float scale, float[] center)	randomly change the model a bit estimates the necessary amount of shaking for each single dimensional distance in the set of matches
final void	TranslationModel2D.shake(Collection<PointMatch> matches, float scale, float[] center)	change the model a bit estimates the necessary amount of shaking for each single dimensional distance in the set of matches
final void	TranslationModel3D.shake(Collection<PointMatch> matches, float scale, float[] center)	change the model a bit estimates the necessary amount of shaking for each single dimensional distance in the set of matches
static final boolean	Model.test(Model model, Collection<PointMatch> candidates, Collection<PointMatch> inliers, double epsilon, double min_inlier_ratio)	Test the Model for a set of point correspondence candidates.