org.ddogleg.fitting.modelset.ransac

Class RansacMulti<Point>

java.lang.Object

org.ddogleg.fitting.modelset.ransac.RansacMulti<Point>

All Implemented Interfaces:

ModelMatcher<java.lang.Object,Point>, ModelMatcherMulti<Point>

public class RansacMulti<Point>
extends java.lang.Object
implements ModelMatcherMulti<Point>

Modification of RANSAC that finds the best fit model and model parameters to a set of data. A model is a mathematical description of a specific object type (e.g. circle, square) and model parameters is the description (e.g. radius and center point for a circle). The minimum (or user specified) set of points is drawn from the set of all points. Then for each model parameters are estimated. The model and parameter pair with the largest inliers set is returned.

To change the default behavior of the class for specific applications the child class can override internal functions. Suggestions are shown below.

• checkExitIteration(): Override to provide custom logic for when the RANSAC iteration should stop
• selectMatchSet(org.ddogleg.fitting.modelset.DistanceFromModel<Model, Point>, double, Model): Override to provide custom for how the inlier set is found. Be sure to set up matchToInput[] correctly.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	RansacMulti.ObjectType<Model,Point> Describes a model and RANSAC fit parameters for specific type of object.

Constructor Summary

Constructors

Constructor and Description
RansacMulti(long randSeed, int maxIterations, java.util.List<RansacMulti.ObjectType> objectTypes, java.lang.Class<Point> typePoint) 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	getInlierSize()
int	getInputIndex(int matchIndex) For an item in the match set, return the index of the item in the original input set.
int	getIteration()
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.
int	getModelIndex() Indicates which model was found to best fit the points.
java.lang.Object	getModelParameters() Model for the match set
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.
void	setMaxIterations(int maxIterations)
void	setSampleSize(int sampleSize) Override the number of points that are sampled and used to generate models.

Methods inherited from class java.lang.Object

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

Constructor Detail

RansacMulti

public RansacMulti(long randSeed,
                   int maxIterations,
                   java.util.List<RansacMulti.ObjectType> objectTypes,
                   java.lang.Class<Point> typePoint)

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.

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

objectTypes - Description of the different types of objects it can detect

typePoint - Class of Point

Method Detail

process

public 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.

Specified by:

process in interface ModelMatcher<java.lang.Object,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.

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<java.lang.Object,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<java.lang.Object,Point>

Parameters:

matchIndex - Index of an element in the match set.

Returns:

Index of the same element in the original input list.

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<java.lang.Object,Point>

Returns:

Quality of fit to matched set of points

getModelParameters

public java.lang.Object getModelParameters()

Description copied from interface: ModelMatcher

Model for the match set

Specified by:

getModelParameters in interface ModelMatcher<java.lang.Object,Point>

Returns:

model.

getModelIndex

public int getModelIndex()

Description copied from interface: ModelMatcherMulti

Indicates which model was found to best fit the points. The index is implementation specific and is likely to refer to the index inside a list.

Specified by:

getModelIndex in interface ModelMatcherMulti<Point>

Returns:

Index of selected model.

getInlierSize

public int getInlierSize()

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<java.lang.Object,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.

getIteration

public int getIteration()