Package com.irurueta.numerical.robust

package com.irurueta.numerical.robust

This package contains robust estimators that can be used to discard outliers for cases where a model of the data is known (i.e. estimating lines, planes or many other geometric objects, etc.) This package contains implementations for the following algorithms: - RANSAC - LMedS - MSAC - PROSAC - PROMedS

Related Packages

Package	Description
com.irurueta.numerical	This library contains packages for:

Class	Description
FastRandomSubsetSelector	This class computes indices of subsets of samples using a uniform randomizer to pick random samples as fast as possible.
InliersData	Base class defining inlier data for a robust estimator.
InvalidSubsetRangeException	Raised if provided range of samples to pick subsets from is invalid.
InvalidSubsetSizeException	Raised if an invalid subset size is requested on a subset selector
LMedSRobustEstimator<T>	This class implements LMedS (Least Median of Squares) algorithm to robustly estimate a data model.
LMedSRobustEstimator.LMedSInliersData	Contains data related to inliers estimated in one iteration.
LMedSRobustEstimatorListener<T>	Listener to get data samples and residuals for LMedS method.
MSACRobustEstimator<T>	This class implements MSAC (Median SAmple Consensus) algorithm to robustly estimate a data model.
MSACRobustEstimator.MSACInliersData	Contains data related to inliers estimated in one iteration.
MSACRobustEstimatorListener<T>	Listener to get data samples and residuals for MSAC method
NotEnoughSamplesException	Raised if there aren't enough samples to make a computation.
PROMedSRobustEstimator<T>	This class implements PROMedS (PROgressive least Median Sample) algorithm to robustly estimate a data model.
PROMedSRobustEstimator.PROMedSInliersData	Contains data related to inliers estimated in one iteration.
PROMedSRobustEstimatorListener<T>	Listener to get data samples and residuals for PROMedS method.
PROSACRobustEstimator<T>	This class implements PROSAC (PROgressive random SAmple Consensus) algorithm to robustly estimate a data model.
PROSACRobustEstimator.PROSACInliersData	Contains data related to estimated inliers.
PROSACRobustEstimatorListener<T>	Listener to get data samples and residuals for PROSAC method
RANSACRobustEstimator<T>	This class implements RANSAC (RANdom SAmple Consensus) algorithm to robustly estimate a data model.
RANSACRobustEstimator.RANSACInliersData	Contains data related to estimated inliers.
RANSACRobustEstimatorListener<T>	Listener to get data samples and residuals for RANSAC method
RobustEstimator<T>	Robust estimator to estimate some object in a robust manner
RobustEstimatorException	Raised if estimation on a RobustEstimator fails.
RobustEstimatorListener<T>	Listener to be notified of events on a robust estimator such as when estimation starts, ends or when progress changes.
RobustEstimatorMethod	Enumerator containing different robust estimation algorithms.
SubsetSelector	Base class to pick subsets of samples.
SubsetSelectorException	Raised if subset selection of samples fails.
SubsetSelectorType	Enumerator containing supported types of subset selectors to pick random samples for robust estimators.
WeightSelection	Class containing the selection that was made on a weighted algorithm.