Collaboration diagram for RANSAC robust estimation:

Classes
struct	tag::Point4SE3Estimation< ImagePlaneZ >

struct	tag::Homography

struct	tag::AffineHomography

struct	tag::CameraRotation

struct	tag::PlaneFromPoints

Functions
template<class Obs , class Trans , class Tol >
size_t	tag::find_RANSAC_inliers (const std::vector< Obs > &observations, const Tol &tolerance, size_t N, Trans &best, std::vector< bool > &inlier, int sample_size=Trans::hypothesis_size)

template<class Obs , class Trans , class Tol >
size_t	tag::find_RANSAC_inliers (const std::vector< Obs > &observations, int sample_size, const Tol &tolerance, size_t N, Trans &best, std::vector< bool > &inlier)

template<class Obs , class Trans , class Tol >
double	tag::find_MSAC_inliers (const std::vector< Obs > &observations, const Tol &tolerance, size_t N, Trans &best, std::vector< bool > &inlier, int sample_size=Trans::hypothesis_size)

template<class Obs , class Trans , class Tol >
double	tag::find_MSAC_inliers (const std::vector< Obs > &observations, int sample_size, const Tol &tolerance, size_t N, Trans &best, std::vector< bool > &inlier)

template<class Obs , class Trans , class Tol , class Prob >
size_t	tag::find_RANSAC_inliers_guided_breadth_first (const std::vector< Obs > &observations, const Prob &prob, const Tol &tolerance, size_t N, size_t block_size, Trans &best, std::vector< bool > &inlier, int sample_size=Trans::hypothesis_size)

template<class Obs , class Trans , class Tol , class Prob >
size_t	tag::find_RANSAC_inliers_guided_breadth_first (const std::vector< Obs > &observations, const Prob &prob, int sample_size, const Tol &tolerance, size_t N, size_t block_size, Trans &best, std::vector< bool > &inlier)

template<class Obs , class Trans , class Tol >
size_t	tag::find_RANSAC_inliers_breadth_first (const std::vector< Obs > &observations, const Tol &tolerance, size_t N, size_t block_size, Trans &best, std::vector< bool > &inlier, int sample_size=Trans::hypothesis_size)

template<class Obs , class Trans , class Tol >
size_t	tag::find_RANSAC_inliers_breadth_first (const std::vector< Obs > &observations, int sample_size, const Tol &tolerance, size_t N, size_t block_size, Trans &best, std::vector< bool > &inlier)

template<class T , class U >
void	tag::remove_if_false (std::vector< T > &v, const std::vector< U > &flag)

Detailed Description

This group contains a set of RANSAC implementations to estimate an inlier set from a set of correspondences under a transformation. The functions are both templated on the correspondence data type and the estimator for the transformation.

Function Documentation

template<class Obs , class Trans , class Tol >

double tag::find_MSAC_inliers	(	const std::vector< Obs > &	observations,
		const Tol &	tolerance,
		size_t	N,
		Trans &	best,
		std::vector< bool > &	inlier,
		int	sample_size = `Trans::hypothesis_size`
	)

basic MSAC implementation. The function is templated on the observation data type and the transformation data type which must conform to the following interface:

class Estimator {
    Estimator();
    // Estimate from a sequence of observations
    template <class It> bool estimate(It begin, It End);
    // return the score for the given observation
    template <class Obs, class Tol> double score(const Obs& obs) const;
    // the number of observations to estimate one hypothesis
    static const int hypothesis_size = XXX;
};

see the file ransac_estimators.h for some Estimator classes for various transformations.

Parameters

[in]	observations	a vector of observations (usually point matches)
[in]	sample_size	the number of samples used estimate a transformation
[in]	tolerance	the tolerance (passed with each observation) to the transformation to check for inliers
[in]	N	the number of hypotheses to test
[out]	best	the transformation hypothesis with the highest inlier count
[out]	inlier	a vector of bools that describes the inlier set of the winning hypothesis

Returns: the score of the winning hypothesis

References tag::randomTuple().

Referenced by tag::find_MSAC_inliers().

template<class Obs , class Trans , class Tol >

double tag::find_MSAC_inliers	(	const std::vector< Obs > &	observations,
		int	sample_size,
		const Tol &	tolerance,
		size_t	N,
		Trans &	best,
		std::vector< bool > &	inlier
	)

backwards compatibility interface to find_MSAC_inliers

Deprecated:

References tag::find_MSAC_inliers().

template<class Obs , class Trans , class Tol >

size_t tag::find_RANSAC_inliers	(	const std::vector< Obs > &	observations,
		const Tol &	tolerance,
		size_t	N,
		Trans &	best,
		std::vector< bool > &	inlier,
		int	sample_size = `Trans::hypothesis_size`
	)

basic RANSAC implementation. The function is templated on the observation data type and the transformation data type which must conform to the following interface:

class Estimator {
    Estimator();
    // Estimate from a sequence of observations
    template <class It> bool estimate(It begin, It End);
    // Check whether the given observation is an inlier for this estimate (with specified tolerance)
    template <class Obs, class Tol> bool isInlier(const Obs& obs, const Tol& tolerance) const;
    // the number of observations to estimate one hypothesis
    static const int hypothesis_size = XXX;
};

see the file ransac_estimators.h for some Estimator classes for various transformations.

Parameters

[in]	observations	a vector of observations (usually point matches)
[in]	sample_size	the number of samples used estimate a transformation
[in]	tolerance	the tolerance (passed with each observation) to the transformation to check for inliers
[in]	N	the number of hypotheses to test
[out]	best	the transformation hypothesis with the highest inlier count
[out]	inlier	a vector of bools that describes the inlier set of the winning hypothesis

Returns: the number of inliers for the winning hypothesis

References tag::randomTuple().

Referenced by tag::find_RANSAC_inliers().

template<class Obs , class Trans , class Tol >

size_t tag::find_RANSAC_inliers	(	const std::vector< Obs > &	observations,
		int	sample_size,
		const Tol &	tolerance,
		size_t	N,
		Trans &	best,
		std::vector< bool > &	inlier
	)

backwards compatibility interface to find_RANSAC_inliers

Deprecated:

References tag::find_RANSAC_inliers().

template<class Obs , class Trans , class Tol >

size_t tag::find_RANSAC_inliers_breadth_first	(	const std::vector< Obs > &	observations,
		const Tol &	tolerance,
		size_t	N,
		size_t	block_size,
		Trans &	best,
		std::vector< bool > &	inlier,
		int	sample_size = `Trans::hypothesis_size`
	)

Breadth-first RANSAC implementation. The function is templated on the observation data type, the tolerance for inliers, and the transformation data type which must conform to the following interface:

class Estimator {
    Estimator();
    // Estimate from a sequence of observations
    template <class It> bool estimate(It begin, It End);
    // Check whether the given observation is an inlier for this estimate (with specified tolerance)
    template <class Obs, class Tol> bool isInlier(const Obs& obs, const Tol& tolerance) const;
    // the number of observations to estimate one hypothesis
    static const int hypothesis_size = XXX;
};

All hypotheses are generated first, and preemptively discarded as more observations are examined. see the file ransac_estimators.h for some Estimator classes for various transformations.

Parameters

[in]	observations	a vector of observations (usually point matches)
[in]	sample_size	the number of samples used estimate a transformation
[in]	tolerance	the tolerance (passed with each observation) to the transformation to check for inliers
[in]	N	the number of hypotheses to test
[in]	block_size	the number of hypotheses to test in a block (between culling hypotheses)
[out]	best	the transformation hypothesis with the highest inlier count
[out]	inlier	a vector of bools that describes the inlier set of the winning hypothesis

Returns: the number of inliers for the winning hypothesis

References tag::getShrinkRatio(), and tag::randomTuple().

Referenced by tag::find_RANSAC_inliers_breadth_first().

template<class Obs , class Trans , class Tol >

size_t tag::find_RANSAC_inliers_breadth_first	(	const std::vector< Obs > &	observations,
		int	sample_size,
		const Tol &	tolerance,
		size_t	N,
		size_t	block_size,
		Trans &	best,
		std::vector< bool > &	inlier
	)

backwards compatibility interface to find_RANSAC_inliers_breadth_first

Deprecated:

References tag::find_RANSAC_inliers_breadth_first().

template<class Obs , class Trans , class Tol , class Prob >

size_t tag::find_RANSAC_inliers_guided_breadth_first	(	const std::vector< Obs > &	observations,
		const Prob &	prob,
		const Tol &	tolerance,
		size_t	N,
		size_t	block_size,
		Trans &	best,
		std::vector< bool > &	inlier,
		int	sample_size = `Trans::hypothesis_size`
	)

Guided breadth-first RANSAC implementation. The function is templated on the observation data type, the probability density for observation correctness, the tolerance for inliers, and the transformation data type which must conform to the following interface:

class Estimator {
    Estimator();
    // Estimate from a sequence of observations
    template <class It> bool estimate(It begin, It End);
    // Check whether the given observation is an inlier for this estimate (with specified tolerance)
    template <class Obs, class Tol> bool isInlier(const Obs& obs, const Tol& tolerance) const;
    // the number of observations to estimate one hypothesis
    static const int hypothesis_size = XXX;
};

All hypotheses are generated first, and preemptively discarded as more observations are examined. The sample sets for hypotheses are drawn probabilistically using the specified probability density. see the file ransac_estimators.h for some Estimator classes for various transformations.

Parameters

[in]	observations	a vector of observations (usually point matches)
[in]	prob	the probability predicate applied (as a function object) to each observation to compute its prior probability of being correct
[in]	sample_size	the number of samples used estimate a transformation
[in]	tolerance	the tolerance (passed with each observation) to the transformation to check for inliers
[in]	N	the number of hypotheses to test
[in]	block_size	the number of hypotheses to test in a block (between culling hypotheses)
[out]	best	the transformation hypothesis with the highest inlier count
[out]	inlier	a vector of bools that describes the inlier set of the winning hypothesis

Returns: the number of inliers for the winning hypothesis

References tag::getShrinkRatio(), and tag::randomTuple().

Referenced by tag::find_RANSAC_inliers_guided_breadth_first().

template<class Obs , class Trans , class Tol , class Prob >

size_t tag::find_RANSAC_inliers_guided_breadth_first	(	const std::vector< Obs > &	observations,
		const Prob &	prob,
		int	sample_size,
		const Tol &	tolerance,
		size_t	N,
		size_t	block_size,
		Trans &	best,
		std::vector< bool > &	inlier
	)

backwards compatibility interface to find_RANSAC_inliers_guided_breadth_first

Deprecated:

References tag::find_RANSAC_inliers_guided_breadth_first().

template<class T , class U >

void tag::remove_if_false	(	std::vector< T > &	v,
		const std::vector< U > &	flag
	)

helper function for use with ransac functions. It removes elements of an input vector based on another vector which is interpreted as boolean flags signaling removal from the first vector or not.

Parameters

[in,out]	v	vector with data to remove
[in]	flag	vector interpreted as flags, if false the corresponding element from v is removed

Classes

Functions

Detailed Description

Function Documentation