Package com.irurueta.geometry.estimators

Class LMedSConicRobustEstimator

java.lang.Object
com.irurueta.geometry.estimators.ConicRobustEstimator
com.irurueta.geometry.estimators.LMedSConicRobustEstimator
public class LMedSConicRobustEstimator extends ConicRobustEstimator
Finds the best conic for provided collection of 2D points using LMedS algorithm.

  • Field Details

    • DEFAULT_STOP_THRESHOLD

      public static final double DEFAULT_STOP_THRESHOLD
      Default value to 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

    • LMedSConicRobustEstimator

      public LMedSConicRobustEstimator()
      Constructor.

    • LMedSConicRobustEstimator

      public LMedSConicRobustEstimator(List<Point2D> points)
      Constructor with points.
      Parameters:
      points - 2D points to estimate a conic.
      Throws:
      IllegalArgumentException - if provided list of points don't have a size greater or equal than MINIMUM_SIZE.

    • LMedSConicRobustEstimator

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

    • LMedSConicRobustEstimator

      public LMedSConicRobustEstimator(ConicRobustEstimatorListener listener, List<Point2D> points)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      points - 2D points to estimate a conic.
      Throws:
      IllegalArgumentException - if provided list of points don't have a size greater or equal 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 Conic estimate() throws LockedException, NotReadyException, com.irurueta.numerical.robust.RobustEstimatorException
      Estimates a conic using a robust estimator and the best set of 2D points that fit into the locus of the estimated conic found using the robust estimator.
      Specified by:
      estimate in class ConicRobustEstimator
      Returns:
      a conic.
      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 ConicRobustEstimator
      Returns:
      method being used for robust estimation.