类的使用
net.semanticmetadata.lire.imageanalysis.features.local.sift.PointMatch

使用PointMatch的程序包

程序包	说明
net.semanticmetadata.lire.imageanalysis.features.local.sift

net.semanticmetadata.lire.imageanalysis.features.local.sift中PointMatch的使用

返回变量类型为PointMatch的类型的net.semanticmetadata.lire.imageanalysis.features.local.sift中的方法

限定符和类型	方法和说明
static java.util.Vector<PointMatch>	FloatArray2DSIFT.createMatches(java.util.List<SiftFeature> fs1, java.util.List<SiftFeature> fs2, float max_sd, Model model, float max_id) identify corresponding features using spatial constraints
static java.util.ArrayList<PointMatch>	PointMatch.flip(java.util.Collection<PointMatch> matches) flip symmetrically, weight remains unchanged

参数类型为PointMatch的net.semanticmetadata.lire.imageanalysis.features.local.sift中的方法

限定符和类型	方法和说明
boolean	TRModel2D.fit(PointMatch[] min_matches)
boolean	TModel2D.fit(PointMatch[] min_matches)
abstract boolean	Model.fit(PointMatch[] min_matches) fit the model to a minimal set of point correpondences estimates a model to transform match.p2.local to match.p1.world

类型变量类型为PointMatch的net.semanticmetadata.lire.imageanalysis.features.local.sift中的方法参数

限定符和类型	方法和说明
static TRModel2D	TRModel2D.estimateBestModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, float min_epsilon, float max_epsilon, float min_inlier_ratio) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC increase the error as long as not more inliers occur
static TRModel2D	TRModel2D.estimateBestModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, float min_epsilon, float max_epsilon, float min_inlier_ratio) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC increase the error as long as not more inliers occur
static TModel2D	TModel2D.estimateBestModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, float min_epsilon, float max_epsilon, float min_inlier_ratio) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC increase the error as long as not more inliers occur
static TModel2D	TModel2D.estimateBestModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, float min_epsilon, float max_epsilon, float min_inlier_ratio) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC increase the error as long as not more inliers occur
static TRModel2D	TRModel2D.estimateModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, int iterations, float epsilon, float min_inlier_ratio) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC
static TRModel2D	TRModel2D.estimateModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, int iterations, float epsilon, float min_inlier_ratio) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC
static TModel2D	TModel2D.estimateModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, int iterations, float epsilon, float min_inliers) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC
static TModel2D	TModel2D.estimateModel(java.util.List<PointMatch> candidates, java.util.Collection<PointMatch> inliers, int iterations, float epsilon, float min_inliers) estimate the transformation model for a set of feature correspondences containing a high number of outliers using RANSAC
static java.util.ArrayList<PointMatch>	PointMatch.flip(java.util.Collection<PointMatch> matches) flip symmetrically, weight remains unchanged
void	TRModel2D.minimize(java.util.Collection<PointMatch> matches)
void	TModel2D.minimize(java.util.Collection<PointMatch> matches)
abstract void	Model.minimize(java.util.Collection<PointMatch> matches)
void	TRModel2D.shake(java.util.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
void	TModel2D.shake(java.util.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
abstract void	Model.shake(java.util.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
boolean	Model.test(java.util.Collection<PointMatch> candidates, java.util.Collection<PointMatch> inliers, double epsilon, double min_inlier_ratio) test the model for a set of point correspondence candidates clears inliers and fills it with the fitting subset of candidates
boolean	Model.test(java.util.Collection<PointMatch> candidates, java.util.Collection<PointMatch> inliers, double epsilon, double min_inlier_ratio) test the model for a set of point correspondence candidates clears inliers and fills it with the fitting subset of candidates