| Modifier and Type | Method and Description |
|---|---|
<P extends PointMatch> |
SimilarityModel3D.fit(Collection<P> matches) |
| Modifier and Type | Method and Description |
|---|---|
<P extends PointMatch> |
CubicBSplineTransform.fit(Collection<P> matches) |
| Modifier and Type | Field and Description |
|---|---|
Collection<PointMatch> |
Distortion_Correction.PointMatchCollectionAndAffine.pointMatches |
| Modifier and Type | Method and Description |
|---|---|
<P extends PointMatch> |
PolynomialModel2D.fit(Collection<P> pointMatches) |
| Modifier and Type | Method and Description |
|---|---|
void |
Distortion_Correction.evaluateCorrection(List<List<PointMatch>> inliers) |
protected void |
Distortion_Correction.extractSIFTPoints(int index,
List<Feature>[] siftFeatures,
List<List<PointMatch>> inliers,
List<AbstractAffineModel2D<?>> models) |
| Constructor and Description |
|---|
PointMatchCollectionAndAffine(AffineTransform affine,
Collection<PointMatch> pointMatches) |
| Modifier and Type | Method and Description |
|---|---|
ArrayList<PointMatch> |
SimplePointMatchIdentification.assignPointMatches(List<P> target,
List<P> reference) |
List<PointMatch> |
PointMatchIdentification.assignPointMatches(List<P> target,
List<P> reference) |
ArrayList<PointMatch> |
ICP.getAmbigousMatches()
Returns the
ArrayList of ambigous PointMatches indentified in the last ICP iteration, or null if no iteration has been computed yet. |
List<PointMatch> |
ICP.getPointMatches()
|
static ArrayList<PointMatch> |
ICP.removeAmbigousMatches(List<PointMatch> matches)
Detects ambigous (and duplicate)
PointMatches, i.e. |
| Modifier and Type | Method and Description |
|---|---|
void |
ICP.estimateIntialModel(List<PointMatch> matches,
Model<?> model)
Estimates an initial
Model based on some given PointMatches. |
protected static ArrayList<Integer> |
ICP.getOccurences(Point pointTarget,
Point pointReference,
List<PointMatch> list)
|
static ArrayList<PointMatch> |
ICP.removeAmbigousMatches(List<PointMatch> matches)
Detects ambigous (and duplicate)
PointMatches, i.e. |
| Modifier and Type | Field and Description |
|---|---|
protected ArrayList<PointMatch> |
InteractiveMapping.m |
protected ArrayList<PointMatch> |
InteractiveInvertibleCoordinateTransform.m |
| Modifier and Type | Method and Description |
|---|---|
protected static void |
TransformMeshMapping.calculateBoundingBox(ArrayList<PointMatch> pm,
double[] min,
double[] max) |
protected static void |
TransformMeshMapping.calculateBoundingBoxInverse(ArrayList<PointMatch> pm,
double[] min,
double[] max) |
static void |
FeatureTransform.matchFeatures(Collection<Feature> fs1,
Collection<Feature> fs2,
List<PointMatch> matches,
float rod)
Identify corresponding features
|
| Modifier and Type | Method and Description |
|---|---|
static void |
BlockMatching.findMatches(ij.process.FloatProcessor source,
ij.process.FloatProcessor target,
ij.process.FloatProcessor sourceMask,
ij.process.FloatProcessor targetMask,
Model<?> initialModel,
Class<? extends AbstractAffineModel2D<?>> localModelClass,
float maxEpsilon,
float maxScale,
float minR,
float rodR,
float maxCurvatureR,
int meshResolution,
float alpha,
Collection<PointMatch> sourceMatches) |
static Shape |
BlockMatching.illustrateMatches(Collection<PointMatch> matches)
Create a Shape that illustrates a
Collection of
PointMatches. |
static void |
BlockMatching.matchByMaximalPMCC(ij.process.FloatProcessor source,
ij.process.FloatProcessor target,
ij.process.FloatProcessor sourceMask,
ij.process.FloatProcessor targetMask,
double scale,
CoordinateTransform transform,
int blockRadiusX,
int blockRadiusY,
int searchRadiusX,
int searchRadiusY,
float minR,
float rod,
float maxCurvature,
Collection<? extends Point> sourcePoints,
Collection<PointMatch> sourceMatches,
ErrorStatistic observer)
Estimate point correspondences for a
Collection of Points among two images that are
approximately related by an InvertibleCoordinateTransform using
the Pearson product-moment correlation coefficient (PMCC) r of
pixel intensities as similarity measure. |
static void |
BlockMatching.matchByMaximalPMCC(ij.process.FloatProcessor source,
ij.process.FloatProcessor target,
ij.process.FloatProcessor sourceMask,
ij.process.FloatProcessor targetMask,
float scale,
CoordinateTransform transform,
int blockRadiusX,
int blockRadiusY,
int searchRadiusX,
int searchRadiusY,
Collection<? extends Point> sourcePoints,
Collection<PointMatch> sourceMatches,
ErrorStatistic observer)
Estimate point correspondences for a
Collection of Points among two images that are
approximately related by an InvertibleCoordinateTransform using
the Pearson product-moment correlation coefficient (PMCC) r of
pixel intensities as similarity measure. |
protected static void |
BlockMatching.matchByMaximalPMCC(ij.process.FloatProcessor source,
ij.process.FloatProcessor target,
int blockRadiusX,
int blockRadiusY,
int searchRadiusX,
int searchRadiusY,
float minR,
float rod,
float maxCurvature,
Collection<PointMatch> query,
Collection<PointMatch> results) |
protected static void |
BlockMatching.matchByMaximalPMCC(ij.process.FloatProcessor source,
ij.process.FloatProcessor target,
int blockRadiusX,
int blockRadiusY,
int searchRadiusX,
int searchRadiusY,
float minR,
float rod,
float maxCurvature,
Collection<PointMatch> query,
Collection<PointMatch> results) |
static void |
BlockMatching.matchByMaximalPMCCFromPreScaledImages(ij.process.FloatProcessor source_scaled,
ij.process.FloatProcessor target_scaled,
double scale,
int blockRadiusX,
int blockRadiusY,
int searchRadiusX,
int searchRadiusY,
float minR,
float rod,
float maxCurvature,
Collection<? extends Point> sourcePoints,
Collection<PointMatch> sourceMatches)
Estimate point correspondences for a
Collection of Points among two images that are
approximately related by an InvertibleCoordinateTransform using
the Pearson product-moment correlation coefficient (PMCC) r of
pixel intensities as similarity measure. |
static void |
BlockMatching.matchByMinimalSquareDifference(ij.process.FloatProcessor source,
ij.process.FloatProcessor target,
InvertibleCoordinateTransform transform,
int blockRadiusX,
int blockRadiusY,
int searchRadiusX,
int searchRadiusY,
Collection<? extends Point> sourcePoints,
Collection<PointMatch> sourceMatches)
Estimate point correspondences for a
Collection of Points among two images that are
approximately related by an InvertibleCoordinateTransform using
the square difference of pixel intensities as a similarity measure. |
| Modifier and Type | Method and Description |
|---|---|
ArrayList<PointMatch> |
BlockMatching_TestParameters.match(ij.process.FloatProcessor ip1,
ij.process.FloatProcessor ip2,
ij.process.FloatProcessor ip1Mask,
ij.process.FloatProcessor ip2Mask) |
| Modifier and Type | Method and Description |
|---|---|
static <M extends AbstractModel<M> & InverseCoordinateTransform> |
AlignStacksWithLandmarks.createAlignedStack(ij.ImagePlus source,
ij.ImagePlus target,
Collection<PointMatch> pointMatches,
M model) |
protected void |
BlockMatching_TestParameters.display(ArrayList<PointMatch> pm12,
RealPointSampleList<ARGBType> maskSamples,
ij.ImagePlus impTable,
ij.process.ColorProcessor ipTable,
int w,
int h,
int i,
int j) |
protected void |
BlockMatching_TestParameters.filter(ArrayList<PointMatch> pm12) |
protected static RealPointSampleList<ARGBType> |
AbstractBlockMatching.matches2ColorSamples(Iterable<PointMatch> matches) |
protected static RealPointSampleList<ARGBType> |
AbstractBlockMatching.matches2ColorSamples2(Iterable<PointMatch> matches) |
| Modifier and Type | Method and Description |
|---|---|
static List<PointMatch> |
Util.pointRoisToPointMatches(PointRoi sourceRoi,
PointRoi targetRoi) |
| Modifier and Type | Method and Description |
|---|---|
static void |
PointVis.drawLocalPointMatchLines(ij.process.ImageProcessor ip,
Collection<? extends PointMatch> pointMatches,
Color color,
int width,
Rectangle srcRect,
Rectangle dstRect,
double srcMagnification,
double dstMagnification) |
static void |
PointVis.drawWorldPointMatchLines(ij.process.ImageProcessor ip,
Collection<? extends PointMatch> pointMatches,
Color color,
int width,
Rectangle srcRect,
Rectangle dstRect,
double srcMagnification,
double dstMagnification) |
| Modifier and Type | Method and Description |
|---|---|
static Vector<PointMatch> |
FloatArray2DSIFT.createMatches(List<Feature> fs1,
List<Feature> fs2,
double max_sd,
AbstractModel<?> model,
double max_id,
double rod)
identify corresponding features using spatial constraints
|
static List<PointMatch> |
FloatArray2DMOPS.createMatches(List<Feature> fs1,
List<Feature> fs2,
double max_sd,
AbstractModel<?> model,
double max_id,
double rod)
identify corresponding features using spatial constraints
|
static List<PointMatch> |
FloatArray2DMOPS.createMatches(List<Feature> fs1,
List<Feature> fs2,
double rod,
HashMap<Point,Feature> m1,
HashMap<Point,Feature> m2)
Identify corresponding features.
|
static Vector<PointMatch> |
FloatArray2DSIFT.createMatches(List<Feature> fs1,
List<Feature> fs2,
float rod)
Identify corresponding features
|
static List<PointMatch> |
FloatArray2DMOPS.createMatches(List<Feature> fs1,
List<Feature> fs2,
float rod)
Identify corresponding features
|
| Modifier and Type | Method and Description |
|---|---|
static int |
Feature.matchFeatures(List<Feature> fs1,
List<Feature> fs2,
List<PointMatch> matches,
double rod)
Identify corresponding features
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
PointMatchFactory<M extends PointMatch> |
| Modifier and Type | Field and Description |
|---|---|
protected HashMap<AffineModel2D,ArrayList<PointMatch>> |
TransformMesh.av |
protected static PointMatchFactory<PointMatch> |
TransformMesh.defaultPointMatchFactory |
protected Set<PointMatch> |
Tile.matches
|
protected Set<PointMatch> |
MovingLeastSquaresTransform.matches |
protected HashMap<PointMatch,Tile<M>> |
MovingLeastSquaresMesh.pt
Tiles are a collection of PointMatches that share a common
transformation model.
|
protected HashMap<PointMatch,Vertex> |
SpringMesh.pv |
protected HashMap<PointMatch,ArrayList<AffineModel2D>> |
TransformMesh.va |
protected HashMap<Vertex,PointMatch> |
SpringMesh.vp |
| Modifier and Type | Method and Description |
|---|---|
<P extends PointMatch> |
Model.filter(Collection<P> candidates,
Collection<P> inliers)
Call
Model.filter(Collection, Collection, double) with maxTrust = 4 and minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
AbstractModel.filter(Collection<P> candidates,
Collection<P> inliers)
Call
AbstractModel.filter(Collection, Collection, double) with maxTrust = 4 and minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
Model.filter(Collection<P> candidates,
Collection<P> inliers,
double maxTrust)
Call
Model.filter(Collection, Collection, double, int) with minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
AbstractModel.filter(Collection<P> candidates,
Collection<P> inliers,
double maxTrust)
Call
AbstractModel.filter(Collection, Collection, double, int) with minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
Model.filter(Collection<P> candidates,
Collection<P> inliers,
double maxTrust,
int minNumInliers)
Estimate the
Model and filter potential outliers by robust
iterative regression. |
<P extends PointMatch> |
AbstractModel.filter(Collection<P> candidates,
Collection<P> inliers,
double maxTrust,
int minNumInliers)
Estimate the
AbstractModel and filter potential outliers by robust
iterative regression. |
<P extends PointMatch> |
Model.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio)
Call
Model.filterRansac(List, Collection, int, double, double, double)
with maxTrust = 4. |
<P extends PointMatch> |
AbstractModel.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio)
Call
AbstractModel.filterRansac(List, Collection, int, double, double, double)
with maxTrust = 4. |
<P extends PointMatch> |
Model.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio,
double maxTrust)
Call
Model.filterRansac(List, Collection, int, double, double, int, double)
with minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
AbstractModel.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio,
double maxTrust)
Call
AbstractModel.filterRansac(List, Collection, int, double, double, int, double)
with minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
Model.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio,
int minNumInliers)
Call
Model.filterRansac(List, Collection, int, double, double, int, double)
with maxTrust = 4. |
<P extends PointMatch> |
AbstractModel.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio,
int minNumInliers)
Call
AbstractModel.filterRansac(List, Collection, int, double, double, int, double)
with maxTrust = 4. |
<P extends PointMatch> |
Model.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio,
int minNumInliers,
double maxTrust)
|
<P extends PointMatch> |
AbstractModel.filterRansac(List<P> candidates,
Collection<P> inliers,
int iterations,
double maxEpsilon,
double minInlierRatio,
int minNumInliers,
double maxTrust)
Estimate a
AbstractModel from a set with many outliers by first
filtering the worst outliers with RANSAC
\citet[{FischlerB81} and filter potential outliers by robust iterative
regression. |
<P extends PointMatch> |
TranslationModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
TranslationModel2D.fit(Collection<P> matches) |
<P extends PointMatch> |
TranslationModel1D.fit(Collection<P> matches) |
<P extends PointMatch> |
SimilarityModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
SimilarityModel2D.fit(Collection<P> matches)
Closed form weighted least squares solution as described by
\citet{SchaeferAl06} and implemented by Johannes Schindelin.
|
<P extends PointMatch> |
RigidModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
RigidModel2D.fit(Collection<P> matches)
Closed form weighted least squares solution as described by
\citet{SchaeferAl06} and implemented by Johannes Schindelin.
|
<P extends PointMatch> |
Model.fit(Collection<P> matches)
Fit the
Model to a set of data points minimizing the global
transfer error. |
<P extends PointMatch> |
InterpolatedModel.fit(Collection<P> matches) |
<P extends PointMatch> |
InterpolatedAffineModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
InterpolatedAffineModel2D.fit(Collection<P> matches) |
<P extends PointMatch> |
InterpolatedAffineModel1D.fit(Collection<P> matches) |
<P extends PointMatch> |
IdentityModel.fit(Collection<P> matches) |
<P extends PointMatch> |
HomographyModel2D.fit(Collection<P> matches) |
<P extends PointMatch> |
ConstantModel.fit(Collection<P> matches) |
<P extends PointMatch> |
AffineModel3D.fit(Collection<P> matches)
Closed form weighted least squares solution as described by
\citet{SchaeferAl06}.
|
<P extends PointMatch> |
AffineModel2D.fit(Collection<P> matches)
Closed form weighted least squares solution as described by
\citet{SchaeferAl06}.
|
<P extends PointMatch> |
AffineModel1D.fit(Collection<P> matches)
Closed form weighted least squares solution as described by
\citet{SchaeferAl06}.
|
<P extends PointMatch> |
Model.localSmoothnessFilter(Collection<P> candidates,
Collection<P> inliers,
double sigma,
double maxEpsilon,
double maxTrust)
|
<P extends PointMatch> |
AbstractModel.localSmoothnessFilter(Collection<P> candidates,
Collection<P> inliers,
double sigma,
double maxEpsilon,
double maxTrust)
Default implementation of
AbstractModel.localSmoothnessFilter(Collection, Collection, double, double, double). |
<P extends PointMatch> |
Model.ransac(List<P> candidates,
Collection<P> inliers,
int iterations,
double epsilon,
double minInlierRatio)
Call
Model.ransac(List, Collection, int, double, double, int) with
minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
AbstractModel.ransac(List<P> candidates,
Collection<P> inliers,
int iterations,
double epsilon,
double minInlierRatio)
Call
AbstractModel.ransac(List, Collection, int, double, double, int) with
minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
Model.ransac(List<P> candidates,
Collection<P> inliers,
int iterations,
double epsilon,
double minInlierRatio,
int minNumInliers)
Find the
Model of a set of PointMatch candidates
containing a high number of outliers using
RANSAC
\citet[{FischlerB81}. |
<P extends PointMatch> |
AbstractModel.ransac(List<P> candidates,
Collection<P> inliers,
int iterations,
double epsilon,
double minInlierRatio,
int minNumInliers)
Find the
AbstractModel of a set of PointMatch candidates
containing a high number of outliers using
RANSAC
\citet[{FischlerB81}. |
<P extends PointMatch> |
Model.test(Collection<P> candidates,
Collection<P> inliers,
double epsilon,
double minInlierRatio)
Call
Model.test(Collection, Collection, double, double, int) with
minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
AbstractModel.test(Collection<P> candidates,
Collection<P> inliers,
double epsilon,
double minInlierRatio)
Call
AbstractModel.test(Collection, Collection, double, double, int) with
minNumInliers = Model.getMinNumMatches(). |
<P extends PointMatch> |
Model.test(Collection<P> candidates,
Collection<P> inliers,
double epsilon,
double minInlierRatio,
int minNumInliers)
Test the
Model for a set of PointMatch candidates. |
<P extends PointMatch> |
AbstractModel.test(Collection<P> candidates,
Collection<P> inliers,
double epsilon,
double minInlierRatio,
int minNumInliers)
Test the
AbstractModel for a set of PointMatch candidates. |
| Modifier and Type | Method and Description |
|---|---|
PointMatch |
TransformMesh.findClosestSourcePoint(double[] there)
Find the closest source point to a given coordinate.
|
PointMatch |
TransformMesh.findClosestTargetPoint(double[] there)
Find the closest target point to a given coordinate.
|
| Modifier and Type | Method and Description |
|---|---|
static Collection<PointMatch> |
PointMatch.flip(Collection<PointMatch> matches)
Flip symmetrically, weights remains unchanged.
|
HashMap<AffineModel2D,ArrayList<PointMatch>> |
TransformMesh.getAV() |
ArrayList<PointMatch> |
TileConfiguration.getConnectingPointMatches(Tile<?> targetTile,
Tile<?> referenceTile)
Returns an
ArrayList of PointMatch that connect the targetTile and the referenceTile. |
Set<PointMatch> |
Tile.getMatches() |
Set<PointMatch> |
MovingLeastSquaresTransform.getMatches() |
HashMap<PointMatch,ArrayList<AffineModel2D>> |
TransformMesh.getVA() |
HashMap<PointMatch,Tile<M>> |
MovingLeastSquaresMesh.getVerticeModelMap() |
Set<PointMatch> |
MovingLeastSquaresMesh.getVertices() |
Collection<PointMatch> |
Model.icp(List<Point> p,
List<Point> q)
Estimate the best model in terms of the Iterative Closest Point
Algorithm \cite{Zhang94} for matching two point clouds into each other.
|
Collection<PointMatch> |
AbstractModel.icp(List<Point> p,
List<Point> q)
Estimate the best model in terms of the Iterative Closest Point
Algorithm \cite{Zhang94} for matching two point clouds into each other.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
Tile.addMatch(PointMatch match)
Add one
PointMatch. |
void |
MovingLeastSquaresMesh.addMatchWeightedByDistance(PointMatch pm,
double alpha)
Add a PointMatch to all Tiles weighted by its distance to the
corresponding vertex.
|
Tile<?> |
Tile.findConnectedTile(PointMatch match)
Try to find the tile which is connected by a particular
PointMatch. |
boolean |
Tile.removeMatch(PointMatch match)
Remove a
PointMatch. |
void |
TransformMesh.updateAffine(PointMatch p)
Update all affine transformations that would have been affected by a
given
Vertex. |
| Modifier and Type | Method and Description |
|---|---|
boolean |
Tile.addMatches(Collection<PointMatch> more)
Add more
PointMatches. |
void |
TransformMesh.addTriangle(ArrayList<PointMatch> t)
Add a triangle defined by 3 PointMatches that defines an
AffineTransform2D.
|
static void |
PointMatch.apply(Collection<? extends PointMatch> matches,
CoordinateTransform t)
|
static void |
PointMatch.cloneSourcePoints(Collection<PointMatch> matches,
Collection<Point> sourcePoints) |
static void |
PointMatch.cloneTargetPoints(Collection<PointMatch> matches,
Collection<Point> targetPoints) |
void |
Tile.connect(Tile<?> o,
Collection<PointMatch> m)
Connect two tiles by a set of point correspondences
|
static Collection<PointMatch> |
PointMatch.flip(Collection<PointMatch> matches)
Flip symmetrically, weights remains unchanged.
|
static void |
PointMatch.flip(Collection<PointMatch> matches,
Collection<PointMatch> flippedMatches)
Flip all
PointMatches from
matches symmetrically and fill
flippedMatches with them, weights remain
unchanged. |
static void |
PointMatch.flip(Collection<PointMatch> matches,
Collection<PointMatch> flippedMatches)
Flip all
PointMatches from
matches symmetrically and fill
flippedMatches with them, weights remain
unchanged. |
static boolean |
TransformMesh.isInConvexTargetPolygon(ArrayList<PointMatch> pm,
double[] t)
Checks if a location is inside a given polygon at the target side or not.
|
static boolean |
TransformMesh.isInSourcePolygon(ArrayList<PointMatch> pm,
double[] t)
Checks if a location is inside a given polygon at the source side or not.
|
static double |
PointMatch.maxDistance(Collection<PointMatch> matches) |
static double |
PointMatch.meanDistance(Collection<PointMatch> matches) |
void |
MovingLeastSquaresTransform2.setMatches(Collection<PointMatch> matches)
Set the control points.
|
void |
MovingLeastSquaresTransform.setMatches(Collection<PointMatch> matches) |
abstract void |
AbstractMovingLeastSquaresTransform.setMatches(Collection<PointMatch> matches) |
static void |
PointMatch.sourcePoints(Collection<PointMatch> matches,
Collection<Point> sourcePoints) |
static void |
PointMatch.targetPoints(Collection<PointMatch> matches,
Collection<Point> targetPoints) |
| Modifier and Type | Method and Description |
|---|---|
ArrayList<PointMatch> |
AbstractPointDescriptor.getBestPointMatchSet()
The combination of descriptorpoints that yielded the best similarity in the last comparison
|
| Modifier and Type | Method and Description |
|---|---|
Object |
SimplePointDescriptor.fitMatches(ArrayList<PointMatch> matches) |
TranslationInvariantModel<?> |
ModelPointDescriptor.fitMatches(ArrayList<PointMatch> matches) |
Object |
LocalCoordinateSystemPointDescriptor.fitMatches(ArrayList<PointMatch> matches)
Not necessary as the main matching method is overwritten
|
abstract Object |
AbstractPointDescriptor.fitMatches(ArrayList<PointMatch> matches)
Computes a fit between this these
PointMatches, this method is called by the Matcher |
| Modifier and Type | Method and Description |
|---|---|
ArrayList<ArrayList<PointMatch>> |
SubsetMatcher.createCandidates(AbstractPointDescriptor<?,?> pd1,
AbstractPointDescriptor<?,?> pd2) |
ArrayList<ArrayList<PointMatch>> |
SimpleMatcher.createCandidates(AbstractPointDescriptor<?,?> pd1,
AbstractPointDescriptor<?,?> pd2) |
ArrayList<ArrayList<PointMatch>> |
Matcher.createCandidates(AbstractPointDescriptor<?,?> pd1,
AbstractPointDescriptor<?,?> pd2) |
| Modifier and Type | Method and Description |
|---|---|
double |
SubsetMatcher.getNormalizationFactor(ArrayList<PointMatch> matches,
Object fitResult) |
double |
SimpleMatcher.getNormalizationFactor(ArrayList<PointMatch> matches,
Object fitResult) |
double |
ModelPriorSubsetMatcher.getNormalizationFactor(ArrayList<PointMatch> matches,
Object fitResult) |
double |
ModelPriorMatcher.getNormalizationFactor(ArrayList<PointMatch> matches,
Object fitResult) |
double |
Matcher.getNormalizationFactor(ArrayList<PointMatch> matches,
Object fitResult)
Computes a normalization factor for the case that the different set of
PointMatches are not comparable
(for example number of neighbors used is not constant) |
| Modifier and Type | Method and Description |
|---|---|
<P extends PointMatch> |
TranslationInvariantSimilarityModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
TranslationInvariantRigidModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
TranslationInvariantRigidModel2D.fit(Collection<P> matches)
Closed form weighted least squares solution as described by
\citet{SchaeferAl06} and implemented by Johannes Schindelin.
|
<P extends PointMatch> |
TranslationInvariantFixedModel.fit(Collection<P> matches) |
<P extends PointMatch> |
TranslationInvariantAffineModel3D.fit(Collection<P> matches) |
<P extends PointMatch> |
FixedModel.fit(Collection<P> matches) |
| Modifier and Type | Method and Description |
|---|---|
double |
SquareDistance.getSimilarity(ArrayList<PointMatch> matches) |
double |
SimilarityMeasure.getSimilarity(ArrayList<PointMatch> matches) |
double |
ManhattanDistance.getSimilarity(ArrayList<PointMatch> matches) |
double |
LinearDistance.getSimilarity(ArrayList<PointMatch> matches) |
| Modifier and Type | Class and Description |
|---|---|
class |
PointMatchGeneric<P extends Point> |
| Modifier and Type | Method and Description |
|---|---|
ArrayList<PointMatch> |
TileConfigurationSPIM.getConnectingPointMatches(Tile<?> targetTile,
Tile<?> referenceTile)
Returns an
ArrayList of PointMatch that connect the targetTile and the referenceTile. |
| Modifier and Type | Class and Description |
|---|---|
class |
PointMatchStitching |
| Modifier and Type | Field and Description |
|---|---|
protected Set<PointMatch> |
AbstractAffineTile2D.virtualMatches
A set of virtual point correspondences that are used to connect a tile
to the rest of the
TileConfiguration assuming that the initial
layout was correct. |
| Modifier and Type | Method and Description |
|---|---|
protected static Collection<PointMatch> |
Align.deserializePointMatches(Align.Param p,
AbstractAffineTile2D<?> t1,
AbstractAffineTile2D<?> t2) |
protected static ArrayList<PointMatch> |
Util.deserializePointMatches(Project project,
Object key,
String prefix,
long id1,
long id2) |
protected static Collection<PointMatch> |
Align.fetchPointMatches(Align.Param p,
AbstractAffineTile2D<?> t1,
AbstractAffineTile2D<?> t2)
Fetch a
Collection of corresponding
SIFT-features. |
Set<PointMatch> |
AbstractAffineTile2D.getVirtualMatches() |
| Modifier and Type | Method and Description |
|---|---|
boolean |
AbstractAffineTile2D.addVirtualMatch(PointMatch match) |
boolean |
AbstractAffineTile2D.removeVirtualMatch(PointMatch match) |
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractAffineTile2D.commonPointMatches(Tile<?> other,
Collection<PointMatch> commonMatches)
Extract the common PointMatches of two tiles.
|
static MovingLeastSquaresTransform2 |
Align.createMLST(Collection<PointMatch> matches,
double alpha)
Temporary helper method that creates
|
static boolean |
Align.findModel(Model<?> model,
List<PointMatch> candidates,
Collection<PointMatch> inliers,
float maxEpsilon,
float minInlierRatio,
int minNumInliers,
boolean rejectIdentity,
float identityTolerance) |
static boolean |
Align.findModel(Model<?> model,
List<PointMatch> candidates,
Collection<PointMatch> inliers,
float maxEpsilon,
float minInlierRatio,
int minNumInliers,
boolean rejectIdentity,
float identityTolerance) |
static boolean |
Align.findModel(Model<?> model,
List<PointMatch> candidates,
Collection<PointMatch> inliers,
float maxEpsilon,
float minInlierRatio,
int minNumInliers,
boolean rejectIdentity,
float identityTolerance,
boolean multipleHypotheses) |
static boolean |
Align.findModel(Model<?> model,
List<PointMatch> candidates,
Collection<PointMatch> inliers,
float maxEpsilon,
float minInlierRatio,
int minNumInliers,
boolean rejectIdentity,
float identityTolerance,
boolean multipleHypotheses) |
protected static ThinPlateSplineTransform |
ElasticLayerAlignment.makeTPS(Set<PointMatch> matches) |
protected static boolean |
Align.serializePointMatches(Align.Param p,
AbstractAffineTile2D<?> t1,
AbstractAffineTile2D<?> t2,
Collection<PointMatch> m)
Save a
Collection of PointMatches two-sided. |
static boolean |
Util.serializePointMatches(Project project,
Object key,
String prefix,
long id1,
long id2,
Collection<PointMatch> m)
Save a
Collection of PointMatches two-sided. |
| Modifier and Type | Field and Description |
|---|---|
Collection<PointMatch> |
BlockMatchPairCallable.BlockMatchResults.pm12 |
Collection<PointMatch> |
BlockMatchPairCallable.BlockMatchResults.pm21 |
| Constructor and Description |
|---|
BlockMatchResults(Collection<? extends Point> v1,
Collection<? extends Point> v2,
Collection<PointMatch> pm12,
Collection<PointMatch> pm21,
boolean layer1Fixed,
boolean layer2Fixed,
Triple<Integer,Integer,AbstractModel<?>> pair) |
BlockMatchResults(Collection<? extends Point> v1,
Collection<? extends Point> v2,
Collection<PointMatch> pm12,
Collection<PointMatch> pm21,
boolean layer1Fixed,
boolean layer2Fixed,
Triple<Integer,Integer,AbstractModel<?>> pair) |
| Modifier and Type | Method and Description |
|---|---|
void |
RansacRegressionReduceFilter.filter(List<PointMatch> candidates,
Collection<PointMatch> inliers) |
void |
RansacRegressionReduceFilter.filter(List<PointMatch> candidates,
Collection<PointMatch> inliers) |
void |
RansacRegressionFilter.filter(List<PointMatch> candidates,
Collection<PointMatch> inliers) |
void |
RansacRegressionFilter.filter(List<PointMatch> candidates,
Collection<PointMatch> inliers) |
void |
PointMatchFilter.filter(List<PointMatch> candidates,
Collection<PointMatch> inliers) |
void |
PointMatchFilter.filter(List<PointMatch> candidates,
Collection<PointMatch> inliers) |
protected static double[] |
RansacRegressionReduceFilter.minMax(Iterable<PointMatch> matches) |
| Modifier and Type | Field and Description |
|---|---|
ArrayList<PointMatch> |
DescriptorParameters.inliers |
| Modifier and Type | Field and Description |
|---|---|
ArrayList<PointMatch> |
ComparePair.inliers |
| Modifier and Type | Method and Description |
|---|---|
protected static ArrayList<PointMatch> |
Matching.findCorrespondingDescriptors(ArrayList<AbstractPointDescriptor> descriptorsA,
ArrayList<AbstractPointDescriptor> descriptorsB,
float nTimesBetter) |
protected static ArrayList<PointMatch> |
Matching.getCorrespondenceCandidates(double nTimesBetter,
Matcher matcher,
ArrayList<DifferenceOfGaussianPeak<FloatType>> peaks1,
ArrayList<DifferenceOfGaussianPeak<FloatType>> peaks2,
Model<?> model,
int dimensionality,
float zStretching1,
float zStretching2,
String explanation) |
| Modifier and Type | Method and Description |
|---|---|
static void |
Matching.addPointMatches(ArrayList<PointMatch> correspondences,
Tile<?> tileA,
Tile<?> tileB) |
protected static String |
Matching.computeRANSAC(ArrayList<PointMatch> candidates,
ArrayList<PointMatch> inliers,
Model<?> model,
float maxEpsilon) |
protected static String |
Matching.computeRANSAC(ArrayList<PointMatch> candidates,
ArrayList<PointMatch> inliers,
Model<?> model,
float maxEpsilon) |
protected static Model<?> |
Matching.pairwiseMatching(ArrayList<PointMatch> finalInliers,
ArrayList<DifferenceOfGaussianPeak<FloatType>> peaks1,
ArrayList<DifferenceOfGaussianPeak<FloatType>> peaks2,
float zStretching1,
float zStretching2,
DescriptorParameters params,
String explanation) |
protected static void |
Matching.setPointRois(ij.ImagePlus imp1,
ij.ImagePlus imp2,
ArrayList<PointMatch> inliers) |
protected static void |
Matching.writePoints(ArrayList<PointMatch> finalInliers,
DescriptorParameters params,
Model<?> finalModel,
PrintWriter out) |
| Modifier and Type | Method and Description |
|---|---|
static List<PointMatch> |
Register_Virtual_Stack_MT.applyTransformReverse(List<PointMatch> list,
CoordinateTransform t)
Apply a transformation to the second point (P2) of a list of Point matches
|
| Modifier and Type | Method and Description |
|---|---|
static List<PointMatch> |
Register_Virtual_Stack_MT.applyTransformReverse(List<PointMatch> list,
CoordinateTransform t)
Apply a transformation to the second point (P2) of a list of Point matches
|
static void |
Register_Virtual_Stack_MT.fitInliers(Register_Virtual_Stack_MT.Param p,
CoordinateTransform t,
List<PointMatch> inliers)
Fit inliers given a registration model
|
| Modifier and Type | Class and Description |
|---|---|
class |
ExtendedPointMatch |
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