jafar::jmath::RANSAC< NUMTYPE > Class Template Reference

An implementation of the RANSAC algorithm for robust fitting of models to data. More...

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Detailed Description

template<typename NUMTYPE = double>
class jafar::jmath::RANSAC< NUMTYPE >

An implementation of the RANSAC algorithm for robust fitting of models to data.

Definition at line 16 of file ransac.hpp.

#include <ransac.hpp>

List of all members.

Public Types
typedef void(*	RANSAC_fit_functor )(const ublas::matrix< NUMTYPE > &data, const std::vector< size_t > &indices, ublas::vector< ublas::matrix< NUMTYPE > > &fit_models)
	The type of the fitting function passed to jafar::jmath::ransac.
typedef void(*	RANSAC_distance_functor )(const ublas::matrix< NUMTYPE > &data, const ublas::vector< ublas::matrix< NUMTYPE > > &test_models, NUMTYPE distance_threshold, unsigned int &best_model_index, std::vector< size_t > &inlier_indices)
	The type of the distance function passed to jafar::jmath::ransac.
typedef bool(*	RANSAC_degenerate_functor )(const ublas::matrix< NUMTYPE > &data, const std::vector< size_t > &indices)
	The type of the function passed to mrpt::math::ransac.
Static Public Member Functions
static bool	execute (const ublas::matrix< NUMTYPE > &data, RANSAC_fit_functor fit_func, RANSAC_distance_functor dist_func, RANSAC_degenerate_functor degen_func, const NUMTYPE distance_threshold, const unsigned int min_size_samples_to_fit, std::vector< size_t > &out_best_inliers, ublas::matrix< NUMTYPE > &out_best_model, const NUMTYPE prob_good_sample=0.999, const size_t max_iter=200000)
	execution task

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Member Function Documentation

template<typename NUMTYPE = double>

static bool jafar::jmath::RANSAC< NUMTYPE >::execute	(	const ublas::matrix< NUMTYPE > &	data,
		RANSAC_fit_functor	fit_func,
		RANSAC_distance_functor	dist_func,
		RANSAC_degenerate_functor	degen_func,
		const NUMTYPE	distance_threshold,
		const unsigned int	min_size_samples_to_fit,
		std::vector< size_t > &	out_best_inliers,
		ublas::matrix< NUMTYPE > &	out_best_model,
		const NUMTYPE	prob_good_sample = 0.999,
		const size_t	max_iter = 200000
	)		[inline, static]

execution task

Parameters:

data	A DxN matrix with all the observed data. D is the dimensionality of data points and N the number of points.
	This implementation is highly inspired on Peter Kovesi's MATLAB scripts (http://www.csse.uwa.edu.au/~pk).

Returns:

false if no good solution can be found, true on success.

Definition at line 44 of file ransac.hpp.

References JFR_ASSERT, and JFR_DEBUG.

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

• modules/jmath/include/jmath/ransac.hpp