Non-linear optimization routines. There are three methods implemented that include Gauss-Newton, Levenberg-Marquardt and Tukey m-estimator. More...

#include <Optimization.h>

Public Types
typedef void(*	EstimateCallback) (CvMat state, CvMat projection, void *param)
	Pointer to the function that projects the state of the system to the measurements. More...

enum	OptimizeMethod { GAUSSNEWTON, LEVENBERGMARQUARDT, TUKEY_LM }
	Selection between the algorithm used in optimization. Following should be noticed: More...

Public Member Functions
void	CalcJacobian (CvMat x, CvMat J, EstimateCallback Estimate)
	Numerically differentiates and calculates the Jacobian around x. More...

CvMat *	GetErr ()
	Returns the current residual vector. More...

	Optimization (int n_params, int n_meas)
	Constructor. More...

double	Optimize (CvMat parameters, CvMat measurements, double stop, int max_iter, EstimateCallback Estimate, void param=0, OptimizeMethod method=LEVENBERGMARQUARDT, CvMat parameters_mask=0, CvMat J_mat=0, CvMat weights=0)
	Runs the optimization loop with selected parameters. More...

	~Optimization ()

Private Member Functions
double	CalcTukeyWeight (double residual, double c)

double	CalcTukeyWeightSimple (double residual, double c)

Private Attributes
CvMat *	delta

CvMat *	diag

CvMat *	err

void *	estimate_param

CvMat *	J

CvMat *	JtJ

double	lambda

CvMat *	tmp

CvMat *	tmp_par

CvMat *	W

CvMat *	x_minus

CvMat *	x_plus

CvMat *	x_tmp1

CvMat *	x_tmp2

Detailed Description

Non-linear optimization routines. There are three methods implemented that include Gauss-Newton, Levenberg-Marquardt and Tukey m-estimator.

Examples:: SampleOptimization.cpp.

Definition at line 44 of file Optimization.h.

Member Typedef Documentation

typedef void(* alvar::Optimization::EstimateCallback) (CvMat *state, CvMat *projection, void *param)

Pointer to the function that projects the state of the system to the measurements.

Parameters

state	System parameters, e.g. camera parameterization in optical tracking.
projection	The system state projection is stored here. E.g image measurements in optical tracking.
param	Additional parameters to the function. E.g. some constant parameters that are not optimized.

Definition at line 101 of file Optimization.h.

Member Enumeration Documentation

enum alvar::Optimization::OptimizeMethod

Selection between the algorithm used in optimization. Following should be noticed:

GAUSSNEWTON

Enumerator
GAUSSNEWTON
LEVENBERGMARQUARDT
TUKEY_LM

Definition at line 74 of file Optimization.h.

Constructor & Destructor Documentation

alvar::Optimization::Optimization	(	int	n_params,
		int	n_meas
	)

Constructor.

Parameters

n_params	Number of parameters to be optimized.
n_meas	Number of measurements that are observed.

Definition at line 34 of file Optimization.cpp.

alvar::Optimization::~Optimization ( )

Definition at line 51 of file Optimization.cpp.

Member Function Documentation

void alvar::Optimization::CalcJacobian	(	CvMat *	x,
		CvMat *	J,
		EstimateCallback	Estimate
	)

Numerically differentiates and calculates the Jacobian around x.

Parameters

x	The set of parameters around which the Jacobian is evaluated.
J	Resulting Jacobian matrix is stored here.
Estimate	The function to be differentiated.

Definition at line 99 of file Optimization.cpp.

double alvar::Optimization::CalcTukeyWeight	(	double	residual,
		double	c
	)

private

Definition at line 68 of file Optimization.cpp.

double alvar::Optimization::CalcTukeyWeightSimple	(	double	residual,
		double	c
	)

private

Definition at line 89 of file Optimization.cpp.

CvMat* alvar::Optimization::GetErr ( )

inline

Returns the current residual vector.

Returns: Pointer to the residual vector.

Definition at line 93 of file Optimization.h.

double alvar::Optimization::Optimize	(	CvMat *	parameters,
		CvMat *	measurements,
		double	stop,
		int	max_iter,
		EstimateCallback	Estimate,
		void *	param = `0`,
		OptimizeMethod	method = `LEVENBERGMARQUARDT`,
		CvMat *	parameters_mask = `0`,
		CvMat *	J_mat = `0`,
		CvMat *	weights = `0`
	)

Runs the optimization loop with selected parameters.

Parameters

parameters	Vector of parameters to be optimized. Initial values should be set.
measurements	Vector of measurements that are observed.
stop	Optimization loop ends as the stop limit is reached. Criteria is calculated as
max_iter	Maximum number of iteration loops that are evaluated if stop is not reached.
Estimate	Pointer to the function that maps the state to the measurements. See EstimateCallback.
method	One of the three possible optimization methods.
parameters_mask	Vector that defines the parameters that are optimized. If vector element is 0, corresponding parameter is not altered.
J_mat	Jacobian matrix. If not given, numerical differentation is used.
weights	Weight vector that can be submitted to give different weights to different measurements. Currently works only with OptimizeMethod::TUKEY_LM.

Examples:: SampleOptimization.cpp.

Definition at line 123 of file Optimization.cpp.

Member Data Documentation

CvMat* alvar::Optimization::delta

private

Definition at line 56 of file Optimization.h.

CvMat* alvar::Optimization::diag

private

Definition at line 53 of file Optimization.h.

CvMat* alvar::Optimization::err

private

Definition at line 55 of file Optimization.h.

void* alvar::Optimization::estimate_param

private

Definition at line 49 of file Optimization.h.

CvMat* alvar::Optimization::J

private

Definition at line 50 of file Optimization.h.

CvMat* alvar::Optimization::JtJ

private

Definition at line 51 of file Optimization.h.

double alvar::Optimization::lambda

private

Definition at line 66 of file Optimization.h.

CvMat* alvar::Optimization::tmp

private

Definition at line 54 of file Optimization.h.

CvMat* alvar::Optimization::tmp_par

private

Definition at line 61 of file Optimization.h.

CvMat* alvar::Optimization::W

private

Definition at line 52 of file Optimization.h.

CvMat* alvar::Optimization::x_minus

private

Definition at line 58 of file Optimization.h.

CvMat* alvar::Optimization::x_plus

private

Definition at line 57 of file Optimization.h.

CvMat* alvar::Optimization::x_tmp1

private

Definition at line 59 of file Optimization.h.

CvMat* alvar::Optimization::x_tmp2

private

Definition at line 60 of file Optimization.h.

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

Public Types

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

Member Enumeration Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation