Package com.irurueta.geometry.estimators

Class LMedSEuclideanTransformation2DRobustEstimator

java.lang.Object
com.irurueta.geometry.estimators.EuclideanTransformation2DRobustEstimator
com.irurueta.geometry.estimators.LMedSEuclideanTransformation2DRobustEstimator
public class LMedSEuclideanTransformation2DRobustEstimator extends EuclideanTransformation2DRobustEstimator
Finds the best Euclidean 2D transformation for provided collections of matched 2D points using LMedS algorithm.

  • Field Details

    • DEFAULT_STOP_THRESHOLD

      public static final double DEFAULT_STOP_THRESHOLD
      Default value ot be used for stop threshold. Stop threshold can be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.
      See Also:

    • MIN_STOP_THRESHOLD

      public static final double MIN_STOP_THRESHOLD
      Minimum allowed stop threshold value.
      See Also:

    • stopThreshold

      private double stopThreshold
      Threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.

  • Constructor Details

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator()
      Constructor.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(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 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.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(EuclideanTransformation2DRobustEstimatorListener listener)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(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.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(boolean weakMinimumSizeAllowed)
      Constructor.
      Parameters:
      weakMinimumSizeAllowed - true allows 2 points, false requires 3.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(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 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 2 points, false requires 3.
      Throws:
      IllegalArgumentException - if provided lists of points don't have the same size or their size is smaller than MINIMUM_SIZE.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(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 2 points, false requires 3.

    • LMedSEuclideanTransformation2DRobustEstimator

      public LMedSEuclideanTransformation2DRobustEstimator(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 2 points, false requires 3.
      Throws:
      IllegalArgumentException - if provided lists of points don't have the same size or their size is smaller than MINIMUM_SIZE.

  • Method Details

    • getStopThreshold

      public double getStopThreshold()
      Returns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results.
      Returns:
      stop threshold to stop the algorithm prematurely when a certain accuracy has been reached.

    • setStopThreshold

      public void setStopThreshold(double stopThreshold) throws LockedException
      Sets threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough. Once a solution is found that generates a threshold below this value, the algorithm will stop. The stop threshold can be used to prevent the LMedS algorithm iterating too many times in cases where samples have a very similar accuracy. For instance, in cases where proportion of outliers is very small (close to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would iterate for a long time trying to find the best solution when indeed there is no need to do that if a reasonable threshold has already been reached. Because of this behaviour the stop threshold can be set to a value much lower than the one typically used in RANSAC, and yet the algorithm could still produce even smaller thresholds in estimated results
      Parameters:
      stopThreshold - stop threshold to stop the algorithm prematurely when a certain accuracy has been reached
      Throws:
      IllegalArgumentException - if provided value is zero or negative
      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.