opengv::sac::MultiSampleConsensus< PROBLEM_T > Class Template Reference

#include <MultiSampleConsensus.hpp>

Inheritance diagram for opengv::sac::MultiSampleConsensus< PROBLEM_T >:

Public Types
typedef PROBLEM_T	problem_t
typedef problem_t::model_t	model_t

Public Member Functions
	MultiSampleConsensus (int maxIterations=1000, double threshold=1.0, double probability=0.99)
	Constructor. More...
virtual	~MultiSampleConsensus ()
	Destructor.
virtual bool	computeModel (int debug_verbosity_level=0)=0
	Fit the model to the data. More...

Public Attributes
int	max_iterations_
int	iterations_
double	threshold_
double	probability_
model_t	model_coefficients_
std::vector< std::vector< int > >	model_
std::vector< std::vector< int > >	inliers_
std::shared_ptr< PROBLEM_T >	sac_model_

Detailed Description

template<typename PROBLEM_T>
class opengv::sac::MultiSampleConsensus< PROBLEM_T >

Super-class for sample consensus methods, such as Ransac. This one is using multi-indices for homogeneous sampling over groups of samples.

Member Typedef Documentation

template<typename PROBLEM_T >

typedef problem_t::model_t opengv::sac::MultiSampleConsensus< PROBLEM_T >::model_t

The model we trying to fit

template<typename PROBLEM_T >

typedef PROBLEM_T opengv::sac::MultiSampleConsensus< PROBLEM_T >::problem_t

A child of MultiSampleConsensusProblem

Constructor & Destructor Documentation

template<typename PROBLEM_T >

opengv::sac::MultiSampleConsensus< PROBLEM_T >::MultiSampleConsensus	(	int	maxIterations = 1000,
		double	threshold = 1.0,
		double	probability = 0.99
	)

Constructor.

Parameters

[in]	maxIterations	The maximum number of hypothesis generations
[in]	threshold	Some threshold value for classifying samples as an inlier or an outlier.
[in]	probability	The probability of being able to draw at least one sample that is free of outliers (see [15])

Member Function Documentation

template<typename PROBLEM_T >

virtual bool opengv::sac::MultiSampleConsensus< PROBLEM_T >::computeModel ( int  debug_verbosity_level = 0 )

pure virtual

Fit the model to the data.

Parameters

[in]

debug_verbosity_level

Sets the verbosity level.

Returns

bool True if success.

Implemented in opengv::sac::MultiRansac< PROBLEM_T >.

Member Data Documentation

template<typename PROBLEM_T >

std::vector< std::vector<int> > opengv::sac::MultiSampleConsensus< PROBLEM_T >::inliers_

the group-wise indices of the samples that have been clasified as inliers

template<typename PROBLEM_T >

int opengv::sac::MultiSampleConsensus< PROBLEM_T >::iterations_

the current number of iterations

template<typename PROBLEM_T >

int opengv::sac::MultiSampleConsensus< PROBLEM_T >::max_iterations_

the maximum number of iterations

template<typename PROBLEM_T >

std::vector< std::vector<int> > opengv::sac::MultiSampleConsensus< PROBLEM_T >::model_

the group-wise indices for the currently best hypothesis

template<typename PROBLEM_T >

model_t opengv::sac::MultiSampleConsensus< PROBLEM_T >::model_coefficients_

the currently best model coefficients

template<typename PROBLEM_T >

double opengv::sac::MultiSampleConsensus< PROBLEM_T >::probability_

the current probability (defines remaining iterations)

template<typename PROBLEM_T >

std::shared_ptr<PROBLEM_T> opengv::sac::MultiSampleConsensus< PROBLEM_T >::sac_model_

the multi-sample-consensus problem we are trying to solve

template<typename PROBLEM_T >

double opengv::sac::MultiSampleConsensus< PROBLEM_T >::threshold_

the threshold for classifying inliers

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

• include/opengv/sac/MultiSampleConsensus.hpp