Package com.irurueta.geometry.estimators

Class PROMedSDualConicRobustEstimator

java.lang.Object
com.irurueta.geometry.estimators.DualConicRobustEstimator
com.irurueta.geometry.estimators.PROMedSDualConicRobustEstimator
public class PROMedSDualConicRobustEstimator extends DualConicRobustEstimator
Finds the best conic for provided collection of 2D lines using PROMedS 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.

    • qualityScores

      private double[] qualityScores
      Quality scores corresponding to each 2D line. The larger the score value the better the quality of the sample.

  • Constructor Details

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator()
      Constructor.

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator(List<Line2D> lines)
      Constructor with points.
      Parameters:
      lines - 2D lines to estimate a dual conic.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • PROMedSDualConicRobustEstimator

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

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator(DualConicRobustEstimatorListener listener, List<Line2D> lines)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a dual conic.
      Throws:
      IllegalArgumentException - if provided list of lines don't have a size greater or equal than MINIMUM_SIZE.

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator(double[] qualityScores)
      Constructor.
      Parameters:
      qualityScores - quality scores corresponding to each provided line.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 5 lines).

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator(List<Line2D> lines, double[] qualityScores)
      Constructor with points.
      Parameters:
      lines - 2D lines to estimate a dual conic.
      qualityScores - quality scores corresponding to each provided line.
      Throws:
      IllegalArgumentException - if provided list of lines don't have the same size as the list of provided quality scores, or it their size is not greater or equal than MINIMUM_SIZE.

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator(DualConicRobustEstimatorListener listener, double[] qualityScores)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      qualityScores - quality scores corresponding to each provided line.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 5 lines).

    • PROMedSDualConicRobustEstimator

      public PROMedSDualConicRobustEstimator(DualConicRobustEstimatorListener listener, List<Line2D> lines, double[] qualityScores)
      Constructor.
      Parameters:
      listener - listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
      lines - 2D lines to estimate a dual conic.
      qualityScores - quality scores corresponding to each provided line.
      Throws:
      IllegalArgumentException - if provided list of points don't have the same size as the list of provided quality scores, or it their size is not 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. As in LMedS, the stop threshold can be used to prevent the PROMedS 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. As in LMedS, the stop threshold can be used to prevent the PROMedS 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.

    • getQualityScores

      public double[] getQualityScores()
      Returns quality scores corresponding to each provided line. The larger the score value the better the quality of the sampled line.
      Overrides:
      getQualityScores in class DualConicRobustEstimator
      Returns:
      quality scores corresponding to each point.

    • setQualityScores

      public void setQualityScores(double[] qualityScores) throws LockedException
      Sets quality scores corresponding to each provided line. The larger the score value the better the quality of the sampled line.
      Overrides:
      setQualityScores in class DualConicRobustEstimator
      Parameters:
      qualityScores - quality scores corresponding to each line.
      Throws:
      LockedException - if robust estimator is locked because an estimation is already in progress.
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE (i.e. 5 samples).

    • isReady

      public boolean isReady()
      Indicates if estimator is ready to start the conic estimation. This is true when input data (i.e. 2D points and quality scores) are provided and a minimum of MINIMUM_SIZE lines are available
      Overrides:
      isReady in class DualConicRobustEstimator
      Returns:
      true if estimator is ready, false otherwise.

    • estimate

      public DualConic estimate() throws LockedException, NotReadyException, com.irurueta.numerical.robust.RobustEstimatorException
      Estimates a dual conic using a robust estimator and the best set of 2D lines that fit into the locus of the estimated dual conic found using the robust estimator.
      Specified by:
      estimate in class DualConicRobustEstimator
      Returns:
      a dual 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 DualConicRobustEstimator
      Returns:
      method being used for robust estimation.

    • internalSetQualityScores

      private void internalSetQualityScores(double[] qualityScores)
      Sets quality scores corresponding to each provided line. This method is used internally and does not check whether instance is locked or not.
      Parameters:
      qualityScores - quality scores to be set.
      Throws:
      IllegalArgumentException - if provided quality scores length is smaller than MINIMUM_SIZE.