org.ddogleg.fitting.modelset.ransac

Class Ransac<Model,Point>

java.lang.Object

org.ddogleg.fitting.modelset.ransac.Ransac<Model,Point>

All Implemented Interfaces:

ModelMatcher<Model,Point>

public class Ransac<Model,Point>
extends java.lang.Object
implements ModelMatcher<Model,Point>

RANSAC is an abbreviation for "RANdom SAmple Consensus" and is an iterative algorithm. The model with the largest set of inliers is found by randomly sampling the set of points and fitting a model. The algorithm terminates when the maximum number of iterations has been reached. An inlier is defined as a point which has an error less than a user specified threshold to the estimated model being considered. The algorithm was first published by Fischler and Bolles in 1981."

Sample Points: By default the minimum number of points are sampled. The user to override this default and set it to any number.

Constructor Summary

Constructors

Constructor and Description
Ransac(long randSeed, ModelManager<Model> modelManager, ModelGenerator<Model,Point> modelGenerator, DistanceFromModel<Model,Point> modelDistance, int maxIterations, double thresholdFit) Creates a new instance of the ransac algorithm.

Method Summary

Modifier and Type	Method and Description
double	getFitQuality() Returns the metric used to evaluate the quality of fit.
int	getInputIndex(int matchIndex) For an item in the match set, return the index of the item in the original input set.
java.util.List<Point>	getMatchSet() A set of points which match the provided parameters.
int	getMaxIterations()
int	getMinimumSize() This is the minimum number of observations which can be input and produce a valid model.
Model	getModelParameters() Model for the match set
double	getThresholdFit()
void	initialize(java.util.List<Point> dataSet) Initialize internal data structures
boolean	process(java.util.List<Point> dataSet) Finds a set of points from the provided list that are a good fit for the internal model and computes the fit parameters for the model.
static <T> void	randomDraw(java.util.List<T> dataSet, int numSample, java.util.List<T> initialSample, java.util.Random rand) Two different methods are used to select the initial set of points depending on if the data set is much larger or smaller than the initial sample size
void	setMaxIterations(int maxIterations)
void	setSampleSize(int sampleSize) Override the number of points that are sampled and used to generate models.
void	setThresholdFit(double thresholdFit)

Methods inherited from class java.lang.Object

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

Constructor Detail

Ransac

public Ransac(long randSeed,
              ModelManager<Model> modelManager,
              ModelGenerator<Model,Point> modelGenerator,
              DistanceFromModel<Model,Point> modelDistance,
              int maxIterations,
              double thresholdFit)

Creates a new instance of the ransac algorithm. The number of points sampled will default to the minimum number. To override this default invoke setSampleSize(int).

Parameters:

randSeed - The random seed used by the random number generator.

modelGenerator - Creates new model(s) given a small number of points.

modelDistance - Computes the difference between a point an a model.

maxIterations - The maximum number of iterations the RANSAC algorithm will perform.

thresholdFit - How close of a fit a points needs to be to the model to be considered a fit.

Method Detail

process

public boolean process(java.util.List<Point> dataSet)

Description copied from interface: ModelMatcher

Finds a set of points from the provided list that are a good fit for the internal model and computes the fit parameters for the model.

Specified by:

process in interface ModelMatcher<Model,Point>

Parameters:

dataSet - Set of points (with noise) that are to be fit.

Returns:

true if it successfully found a solution or false if not.

initialize

public void initialize(java.util.List<Point> dataSet)

Initialize internal data structures

randomDraw

public static <T> void randomDraw(java.util.List<T> dataSet,
                                  int numSample,
                                  java.util.List<T> initialSample,
                                  java.util.Random rand)

Two different methods are used to select the initial set of points depending on if the data set is much larger or smaller than the initial sample size

getMatchSet

public java.util.List<Point> getMatchSet()

Description copied from interface: ModelMatcher

A set of points which match the provided parameters.

Specified by:

getMatchSet in interface ModelMatcher<Model,Point>

Returns:

List of points in the match set.

getInputIndex

public int getInputIndex(int matchIndex)

Description copied from interface: ModelMatcher

For an item in the match set, return the index of the item in the original input set.

Specified by:

getInputIndex in interface ModelMatcher<Model,Point>

Parameters:

matchIndex - Index of an element in the match set.

Returns:

Index of the same element in the original input list.

getModelParameters

public Model getModelParameters()

Description copied from interface: ModelMatcher

Model for the match set

Specified by:

getModelParameters in interface ModelMatcher<Model,Point>

Returns:

model.

getFitQuality

public double getFitQuality()

Description copied from interface: ModelMatcher

Returns the metric used to evaluate the quality of fit. Meaning is implementation specific. Larger or smaller values could be preferred depending on implementation..

Specified by:

getFitQuality in interface ModelMatcher<Model,Point>

Returns:

Quality of fit to matched set of points

getMaxIterations

public int getMaxIterations()

setMaxIterations

public void setMaxIterations(int maxIterations)

getMinimumSize

public int getMinimumSize()

Description copied from interface: ModelMatcher

This is the minimum number of observations which can be input and produce a valid model.

Specified by:

getMinimumSize in interface ModelMatcher<Model,Point>

Returns:

Minimum number of sample points

setSampleSize

public void setSampleSize(int sampleSize)

Override the number of points that are sampled and used to generate models. If this value is not set it defaults to the minimum number.

Parameters:

sampleSize - Number of sample points.

getThresholdFit

public double getThresholdFit()

setThresholdFit

public void setThresholdFit(double thresholdFit)