lti::ransacEstimator::parameters Class Reference

The parameters for the class ransacEstimator. More...

#include <ltiRansacEstimator.h>

Inheritance diagram for lti::ransacEstimator::parameters:

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Collaboration diagram for lti::ransacEstimator::parameters:

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List of all members.

Public Member Functions
	parameters ()
	parameters (const parameters &other)
	~parameters ()
const char *	getTypeName () const
parameters &	copy (const parameters &other)
parameters &	operator= (const parameters &other)
virtual functor::parameters *	clone () const
virtual bool	write (ioHandler &handler, const bool complete=true) const
virtual bool	read (ioHandler &handler, const bool complete=true)
Public Attributes
bool	adaptiveContamination
int	numCorrespondencesPerTrial
bool	checkStop
float	contamination
float	confidence
bool	useMinCorrespondences

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Detailed Description

The parameters for the class ransacEstimator.

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Constructor & Destructor Documentation

lti::ransacEstimator::parameters::parameters

(

)

Default constructor.

Reimplemented from lti::monteCarloEstimator::parameters.

lti::ransacEstimator::parameters::parameters

(

const parameters &

other

)

Copy constructor.

Parameters:

other

the parameters object to be copied

Reimplemented from lti::monteCarloEstimator::parameters.

lti::ransacEstimator::parameters::~parameters

(

)

[virtual]

Destructor.

Reimplemented from lti::monteCarloEstimator::parameters.

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Member Function Documentation

virtual functor::parameters* lti::ransacEstimator::parameters::clone

(

)

const [virtual]

Returns a pointer to a clone of the parameters.

Reimplemented from lti::monteCarloEstimator::parameters.

parameters& lti::ransacEstimator::parameters::copy

(

const parameters &

other

)

Copy the contents of a parameters object.

Parameters:

other

the parameters object to be copied

Returns:

a reference to this parameters object

Reimplemented from lti::monteCarloEstimator::parameters.

const char* lti::ransacEstimator::parameters::getTypeName

(

)

const [virtual]

Returns name of this type.

Reimplemented from lti::monteCarloEstimator::parameters.

parameters& lti::ransacEstimator::parameters::operator=

(

const parameters &

other

)

Copy the contents of a parameters object.

Parameters:

other

the parameters object to be copied

Returns:

a reference to this parameters object

Reimplemented from lti::monteCarloEstimator::parameters.

virtual bool lti::ransacEstimator::parameters::read	(	ioHandler &	handler,
		const bool	complete = true
	)			[virtual]

Read the parameters from the given ioHandler.

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also written, otherwise only the data block will be written.

Returns:

true if write was successful

Reimplemented from lti::monteCarloEstimator::parameters.

virtual bool lti::ransacEstimator::parameters::write	(	ioHandler &	handler,
		const bool	complete = true
	)			const [virtual]

Write the parameters in the given ioHandler.

Parameters:

	handler	the ioHandler to be used
	complete	if true (the default) the enclosing begin/end will be also written, otherwise only the data block will be written.

Returns:

true if write was successful

Reimplemented from lti::monteCarloEstimator::parameters.

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Member Data Documentation

bool lti::ransacEstimator::parameters::adaptiveContamination

Adjust the degree of contamination after each successfull guess.

The contamination is only decreased and never increased. This parameter effects the number of iterations performed. The functor will always terminate after at most the maximum iterations specified in the parameters, though.

If adaptive contamination is on, the applies return true even if the detected inliers suggest a contamination above the parametrized contamination.

Default: false.

bool lti::ransacEstimator::parameters::checkStop

If true the algorithm stops as soon as a transformation is found, whose number of outliers is below the expected contamination.

The expected contamination is supplied as a parameter and not changed adaptively.

Default: true.

float lti::ransacEstimator::parameters::confidence

The number of trials in adaptive mode depends on the estimated contamination and the confidence, under which the result is correct.

Default: .99

float lti::ransacEstimator::parameters::contamination

The expected degree of contamination.

Default: .5

int lti::ransacEstimator::parameters::numCorrespondencesPerTrial

The number of correspondences drawn at each trial to estimate the transform.

Literature advises to use the minimum number correspondences that are required which is proved optimal under a statistical context.

Default: true.

bool lti::ransacEstimator::parameters::useMinCorrespondences

If true the minimum required number of points/correspondences is used no matter which value numCorrespondencesPerTrial has.

Default: true.

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The documentation for this class was generated from the following file:

• ltiRansacEstimator.h