org.openimaj.math.geometry.transforms

Enum FundamentalRefinement

java.lang.Object

java.lang.Enum<FundamentalRefinement>

org.openimaj.math.geometry.transforms.FundamentalRefinement

All Implemented Interfaces:

Serializable, Comparable<FundamentalRefinement>

public enum FundamentalRefinement
extends Enum<FundamentalRefinement>

Refinement of fundamental matrix estimates using non-linear optimisation (Levenberg-Marquardt) under different geometric distance/error assumptions.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected static class	FundamentalRefinement.Base
protected static class	FundamentalRefinement.F12Epipolar Based on the following matlab:
protected static class	FundamentalRefinement.F12Sampson Based on the following matlab:
protected static class	FundamentalRefinement.F13Epipolar Based on the following matlab:
protected static class	FundamentalRefinement.F13Sampson Based on the following matlab:
protected static class	FundamentalRefinement.F23Epipolar Based on the following matlab:
protected static class	FundamentalRefinement.F23Sampson Based on the following matlab:
protected static class	FundamentalRefinement.FastSolveNormal3x2
protected static class	FundamentalRefinement.Parameterisation Parameterisations of the fundamental matrix that preserve the rank-2 constraint by writing one of the rows as a weighted combination of the other two.

Enum Constant Summary

Enum Constants

Enum Constant and Description
EPIPOLAR Minimise the symmetric epipolar distance
NONE Don't perform any refinement and just return the initial input matrix
SAMPSON Minimise the Sampson distance (the first-order estimate of geometric error)

Method Summary

Methods

Modifier and Type	Method and Description
protected abstract org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction	getFunctions(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data, FundamentalRefinement.Parameterisation p)
Jama.Matrix	refine(Jama.Matrix initial, List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data) Refine an initial guess at the homography that takes the first points in data to the second using non-linear Levenberg Marquardt optimisation.
static FundamentalRefinement	valueOf(String name) Returns the enum constant of this type with the specified name.
static FundamentalRefinement[]	values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods inherited from class java.lang.Enum

clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Enum Constant Detail

NONE

public static final FundamentalRefinement NONE

Don't perform any refinement and just return the initial input matrix

EPIPOLAR

public static final FundamentalRefinement EPIPOLAR

Minimise the symmetric epipolar distance

SAMPSON

public static final FundamentalRefinement SAMPSON

Minimise the Sampson distance (the first-order estimate of geometric error)

Method Detail

values

public static FundamentalRefinement[] values()

Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:

for (FundamentalRefinement c : FundamentalRefinement.values())
    System.out.println(c);

Returns:

an array containing the constants of this enum type, in the order they are declared

valueOf

public static FundamentalRefinement valueOf(String name)

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Parameters:

name - the name of the enum constant to be returned.

Returns:

the enum constant with the specified name

Throws:

IllegalArgumentException - if this enum type has no constant with the specified name

NullPointerException - if the argument is null

getFunctions

protected abstract org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction getFunctions(List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data,
                                                                                      FundamentalRefinement.Parameterisation p)

refine

public Jama.Matrix refine(Jama.Matrix initial,
                 List<? extends IndependentPair<? extends Point2d,? extends Point2d>> data)

Refine an initial guess at the homography that takes the first points in data to the second using non-linear Levenberg Marquardt optimisation. The initial guess would normally be computed using the direct linear transform (TransformUtilities.homographyMatrixNorm(List)).

Parameters:

initial - the initial estimate (probably from the DLT technique)

data - the pairs of data points

Returns:

the optimised estimate