Package com.irurueta.geometry.estimators

Class MSACEuclideanTransformation2DRobustEstimator

java.lang.Object

com.irurueta.geometry.estimators.EuclideanTransformation2DRobustEstimator

com.irurueta.geometry.estimators.MSACEuclideanTransformation2DRobustEstimator

public class MSACEuclideanTransformation2DRobustEstimator
extends EuclideanTransformation2DRobustEstimator

Finds the best Euclidean 2D transformation for provided collections of matched 2D points using MSAC algorithm.

Field Summary

Fields

Modifier and Type	Field	Description
static final double	DEFAULT_THRESHOLD	Constant defining default threshold to determine whether points are inliers or not.
static final double	MIN_THRESHOLD	Minimum value that can be set as threshold.
private double	threshold	Threshold to determine whether points are inliers or not when testing possible estimation solutions.

Fields inherited from class com.irurueta.geometry.estimators.EuclideanTransformation2DRobustEstimator

confidence, DEFAULT_CONFIDENCE, DEFAULT_KEEP_COVARIANCE, DEFAULT_MAX_ITERATIONS, DEFAULT_PROGRESS_DELTA, DEFAULT_REFINE_RESULT, DEFAULT_ROBUST_METHOD, inliersData, inputPoints, listener, locked, MAX_CONFIDENCE, MAX_PROGRESS_DELTA, maxIterations, MIN_CONFIDENCE, MIN_ITERATIONS, MIN_PROGRESS_DELTA, MINIMUM_SIZE, outputPoints, progressDelta, refineResult, WEAK_MINIMUM_SIZE

Constructor Summary

Constructors

Constructor	Description
MSACEuclideanTransformation2DRobustEstimator()	Constructor.
MSACEuclideanTransformation2DRobustEstimator(boolean weakMinimumSizeAllowed)	Constructor.
MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener)	Constructor.
MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener, boolean weakMinimumSizeAllowed)	Constructor.
MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener, List<Point2D> inputPoints, List<Point2D> outputPoints)	Constructor with listener and lists of points to be used to estimate an Euclidean 2D transformation.
MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener, List<Point2D> inputPoints, List<Point2D> outputPoints, boolean weakMinimumSizeAllowed)	Constructor with listener and lists of points to be used to estimate an Euclidean 2D transformation.
MSACEuclideanTransformation2DRobustEstimator(List<Point2D> inputPoints, List<Point2D> outputPoints)	Constructor with lists of points to be used to estimate an Euclidean 2D transformation.
MSACEuclideanTransformation2DRobustEstimator(List<Point2D> inputPoints, List<Point2D> outputPoints, boolean weakMinimumSizeAllowed)	Constructor with lists of points to be used to estimate an Euclidean 2D transformation.

Method Summary

Modifier and Type	Method	Description
EuclideanTransformation2D	estimate()	Estimates an Euclidean 2D transformation using a robust estimator and the best set of matched 2D point correspondences found using the robust estimator.
com.irurueta.numerical.robust.RobustEstimatorMethod	getMethod()	Returns method being used for robust estimation.
protected double	getRefinementStandardDeviation()	Gets standard deviation used for Levenberg-Marquardt fitting during refinement.
double	getThreshold()	Returns threshold to determine whether points are inliers or not when testing possible estimation solutions.
void	setThreshold(double threshold)	Sets threshold to determine whether points are inliers or not when testing possible estimation solutions.

Methods inherited from class com.irurueta.geometry.estimators.EuclideanTransformation2DRobustEstimator

attemptRefine, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, getConfidence, getCovariance, getInliersData, getInputPoints, getListener, getMaxIterations, getMinimumPoints, getOutputPoints, getProgressDelta, getQualityScores, isCovarianceKept, isListenerAvailable, isLocked, isReady, isResultRefined, isWeakMinimumSizeAllowed, setConfidence, setCovarianceKept, setListener, setMaxIterations, setPoints, setProgressDelta, setQualityScores, setResultRefined, setWeakMinimumSizeAllowed

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

DEFAULT_THRESHOLD

public static final double DEFAULT_THRESHOLD

Constant defining default threshold to determine whether points are inliers or not. By default, 1.0 is considered a good value for cases where measures are done on pixels, since typically the minimum resolution is 1 pixel.

See Also:

• Constant Field Values

MIN_THRESHOLD

public static final double MIN_THRESHOLD

Minimum value that can be set as threshold. Threshold must be strictly greater than 0.0.

See Also:

• Constant Field Values

threshold

private double threshold

Threshold to determine whether points are inliers or not when testing possible estimation solutions. The threshold refers to the amount of error (i.e. distance) a possible solution has on a matched pair of points.

Constructor Details

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator()

Constructor.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(List<Point2D> inputPoints,
 List<Point2D> outputPoints)

Constructor with lists of points to be used to estimate an Euclidean 2D transformation. Points in the list located at the same position are considered to be matched. Hence, both lists must have the same size, and their size must be greater or equal than MINIMUM_SIZE.

Parameters:

inputPoints - list of input points ot be used to estimate an Euclidean 2D transformation.

outputPoints - list of output points to be used to estimate an Euclidean 2D transformation.

Throws:

IllegalArgumentException - if provided lists of points don't have the same size or their size is smaller than MINIMUM_SIZE.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener)

Constructor.

Parameters:

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener,
 List<Point2D> inputPoints,
 List<Point2D> outputPoints)

Constructor with listener and lists of points to be used to estimate an Euclidean 2D transformation. Points in the list located at the same position are considered to be matched. Hence, both lists must have the same size, and their size must be greater or equal than MINIMUM_SIZE.

Parameters:

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

inputPoints - list of input points to be used to estimate an Euclidean 2D transformation.

outputPoints - list of output points to be used to estimate an Euclidean 2D transformation.

Throws:

IllegalArgumentException - if provided lists of points don't have the same size or their size is smaller than MINIMUM_SIZE.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(boolean weakMinimumSizeAllowed)

Constructor.

Parameters:

weakMinimumSizeAllowed - true allows 3 points, false requires 4.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(List<Point2D> inputPoints,
 List<Point2D> outputPoints,
 boolean weakMinimumSizeAllowed)

Constructor with lists of points to be used to estimate an Euclidean 2D transformation. Points in the list located at the same position are considered to be matched. Hence, both lists must have the same size, and their size must be greater or equal than MINIMUM_SIZE.

Parameters:

inputPoints - list of input points ot be used to estimate an Euclidean 2D transformation.

outputPoints - list of output points to be used to estimate an Euclidean 2D transformation.

weakMinimumSizeAllowed - true allows 3 points, false requires 4.

Throws:

IllegalArgumentException - if provided lists of points don't have the same size or their size is smaller than MINIMUM_SIZE.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener,
 boolean weakMinimumSizeAllowed)

Constructor.

Parameters:

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

weakMinimumSizeAllowed - true allows 3 points, false requires 4.

MSACEuclideanTransformation2DRobustEstimator

public MSACEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener,
 List<Point2D> inputPoints,
 List<Point2D> outputPoints,
 boolean weakMinimumSizeAllowed)

Constructor with listener and lists of points to be used to estimate an Euclidean 2D transformation. Points in the list located at the same position are considered to be matched. Hence, both lists must have the same size, and their size must be greater or equal than MINIMUM_SIZE.

Parameters:

listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

inputPoints - list of input points to be used to estimate an Euclidean 2D transformation.

outputPoints - list of output points to be used to estimate an Euclidean 2D transformation.

weakMinimumSizeAllowed - true allows 3 points, false requires 4.

Throws:

IllegalArgumentException - if provided lists of points don't have the same size or their size is smaller than MINIMUM_SIZE.

Method Details

getThreshold

public double getThreshold()

Returns threshold to determine whether points are inliers or not when testing possible estimation solutions. The threshold refers to the amount of error (i.e. Euclidean distance) a possible solution has on a matched pair of points.

Returns:

threshold to determine whether points are inliers or not when testing possible estimation solutions.

setThreshold

public void setThreshold(double threshold)
                  throws LockedException

Sets threshold to determine whether points are inliers or not when testing possible estimation solutions. The threshold refers to the amount of error (i.e. Euclidean distance) a possible solution has on a matched pair of points.

Parameters:

threshold - threshold to determine whether points are inliers or not.

Throws:

IllegalArgumentException - if provided value is equal or less than zero.

LockedException - if robust estimator is locked because an estimation is already in progress.

estimate

public EuclideanTransformation2D estimate()
                                   throws LockedException,
NotReadyException,
com.irurueta.numerical.robust.RobustEstimatorException

Estimates an Euclidean 2D transformation using a robust estimator and the best set of matched 2D point correspondences found using the robust estimator.

Specified by:

estimate in class EuclideanTransformation2DRobustEstimator

Returns:

an Euclidean 2D transformation.

Throws:

LockedException - if robust estimator is locked because an estimation is already in progress.

NotReadyException - if provided input data is not enough to start the estimation.

com.irurueta.numerical.robust.RobustEstimatorException - if estimation fails for any reason (i.e. numerical instability, no solution available, etc).

getMethod

public com.irurueta.numerical.robust.RobustEstimatorMethod getMethod()

Returns method being used for robust estimation.

Specified by:

getMethod in class EuclideanTransformation2DRobustEstimator

Returns:

method being used for robust estimation.

getRefinementStandardDeviation

protected double getRefinementStandardDeviation()

Gets standard deviation used for Levenberg-Marquardt fitting during refinement. Returned value gives an indication of how much variance each residual has. Typically, this value is related to the threshold used on each robust estimation, since residuals of found inliers are within the range of such threshold.

Specified by:

getRefinementStandardDeviation in class EuclideanTransformation2DRobustEstimator

Returns:

standard deviation used for refinement.