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