Generic RANSAC (RAndom SAmpling Consensus) Implementation. More...

#include <RansacFitter.h>

Classes
struct	Result
	result structure More...

Public Types
typedef std::vector< DataPoint >	DataSet
	DataSet type (just a set of DataPoint instances) More...

typedef utils::Function< Model, const DataSet & >	ModelFitting
	Function for the fitting module (gets a dataset and returns the fitted model) More...

typedef utils::Function< icl64f, const Model &, const DataPoint & >	PointError
	Error function for single points. More...

Public Member Functions
	RansacFitter ()
	empty constructor (creates a dummy instance) More...

	RansacFitter (int minPointsForModel, int iterations, ModelFitting fitting, PointError err, icl64f maxModelDistance, int minClosePointsForGoodModel, icl64f minErrorExit=0)
	constructor with given parameters More...

const Result &	fit (const DataSet &allPoints)
	fitting function (actual RANSAC algorithm) More...

Private Member Functions
void	find_random_consensus_set (DataSet &currConsensusSet, const DataSet &allPoints, std::vector< int > &usedIndices)
	internal utility method More...

Static Private Member Functions
static bool	find_in (const std::vector< int > &v, int i, int n)
	internal utility method More...

Private Attributes
int	m_minPointsForModel
	minimum points that are used to create a coarse model More...

int	m_iterations
	number of iterations More...

icl64f	m_maxModelDistance
	maximum distance of a point to the model to become an inlier More...

int	m_minClosePointsForGoodModel
	minimum amount of inliers for a 'good' model More...

ModelFitting	m_fitting
	fitting function More...

PointError	m_err
	point-model error function More...

icl64f	m_minErrorExit
	min error criterion for early exit More...

Result	m_result
	internal result buffer More...

Detailed Description

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>
class icl::math::RansacFitter< DataPoint, Model >

Generic RANSAC (RAndom SAmpling Consensus) Implementation.

The RansacFitter provides a generic framework, for RANSAC based model fitting.

Algorithms

The RANSAC Algorithm is well described on Wikipedia

See also: http://de.wikipedia.org/wiki/RANSAC-Algorithmus

Example (Image Convolution)

An example is given in the ICL Manual

Template Parameters

The two tempalte parameters are kept very general. Therefore, there are just a few restrictions for the DataPoint and Model classes.

default constructable
copyable

Member Typedef Documentation

◆ DataSet

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

typedef std::vector<DataPoint> icl::math::RansacFitter< DataPoint, Model >::DataSet

DataSet type (just a set of DataPoint instances)

◆ ModelFitting

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

typedef utils::Function<Model,const DataSet&> icl::math::RansacFitter< DataPoint, Model >::ModelFitting

Function for the fitting module (gets a dataset and returns the fitted model)

◆ PointError

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

typedef utils::Function<icl64f,const Model&,const DataPoint&> icl::math::RansacFitter< DataPoint, Model >::PointError

Error function for single points.

Constructor & Destructor Documentation

◆ RansacFitter() [1/2]

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

icl::math::RansacFitter< DataPoint, Model >::RansacFitter ( )

inline

empty constructor (creates a dummy instance)

◆ RansacFitter() [2/2]

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

icl::math::RansacFitter< DataPoint, Model >::RansacFitter	(	int	minPointsForModel,
		int	iterations,
		ModelFitting	fitting,
		PointError	err,
		icl64f	maxModelDistance,
		int	minClosePointsForGoodModel,
		icl64f	minErrorExit = `0`
	)

inline

constructor with given parameters

The parameters functionality is documented with the analogously named member variables

Member Function Documentation

◆ find_in()

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

static bool icl::math::RansacFitter< DataPoint, Model >::find_in	(	const std::vector< int > &	v,
		int	i,
		int	n
	)

inlinestaticprivate

internal utility method

◆ find_random_consensus_set()

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

void icl::math::RansacFitter< DataPoint, Model >::find_random_consensus_set	(	DataSet &	currConsensusSet,
		const DataSet &	allPoints,
		std::vector< int > &	usedIndices
	)

inlineprivate

internal utility method

◆ fit()

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

const Result& icl::math::RansacFitter< DataPoint, Model >::fit ( const DataSet & allPoints )

inline

fitting function (actual RANSAC algorithm)

Member Data Documentation

◆ m_err

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

PointError icl::math::RansacFitter< DataPoint, Model >::m_err

private

point-model error function

◆ m_fitting

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

ModelFitting icl::math::RansacFitter< DataPoint, Model >::m_fitting

private

fitting function

◆ m_iterations

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

int icl::math::RansacFitter< DataPoint, Model >::m_iterations

private

number of iterations

◆ m_maxModelDistance

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

icl64f icl::math::RansacFitter< DataPoint, Model >::m_maxModelDistance

private

maximum distance of a point to the model to become an inlier

◆ m_minClosePointsForGoodModel

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

int icl::math::RansacFitter< DataPoint, Model >::m_minClosePointsForGoodModel

private

minimum amount of inliers for a 'good' model

◆ m_minErrorExit

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

icl64f icl::math::RansacFitter< DataPoint, Model >::m_minErrorExit

private

min error criterion for early exit

◆ m_minPointsForModel

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

int icl::math::RansacFitter< DataPoint, Model >::m_minPointsForModel

private

minimum points that are used to create a coarse model

◆ m_result

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>

Result icl::math::RansacFitter< DataPoint, Model >::m_result

private

internal result buffer

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

/Users/alneuman/vm/icl/ICLMath/src/ICLMath/RansacFitter.h

Classes

Public Types

Public Member Functions

Private Member Functions

Static Private Member Functions

Private Attributes

Detailed Description

template<class DataPoint = std::vector<float>, class Model = std::vector<float>> class icl::math::RansacFitter< DataPoint, Model >

Algorithms

Example (Image Convolution)

Template Parameters

Member Typedef Documentation

◆ DataSet

◆ ModelFitting

◆ PointError

Constructor & Destructor Documentation

◆ RansacFitter() [1/2]

◆ RansacFitter() [2/2]

Member Function Documentation

◆ find_in()

◆ find_random_consensus_set()

◆ fit()

Member Data Documentation

◆ m_err

◆ m_fitting

◆ m_iterations

◆ m_maxModelDistance

◆ m_minClosePointsForGoodModel

◆ m_minErrorExit

◆ m_minPointsForModel

◆ m_result

template<class DataPoint = std::vector<float>, class Model = std::vector<float>>
class icl::math::RansacFitter< DataPoint, Model >