#include <itkGaussianMembershipFunction.h>

Inheritance diagram for itk::Statistics::GaussianMembershipFunction< TMeasurementVector >:

Collaboration diagram for itk::Statistics::GaussianMembershipFunction< TMeasurementVector >:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef VariableSizeMatrix < double >	CovarianceMatrixType
typedef MeasurementVectorRealType	MeanVectorType
typedef itk::NumericTraits < MeasurementVectorType > ::RealType	MeasurementVectorRealType
typedef Superclass::MeasurementVectorSizeType	MeasurementVectorSizeType
typedef TMeasurementVector	MeasurementVectorType
typedef Superclass::Pointer	MembershipFunctionPointer
typedef SmartPointer< Self >	Pointer
typedef GaussianMembershipFunction	Self
typedef MembershipFunctionBase < TMeasurementVector >	Superclass
Public Types inherited from itk::Statistics::MembershipFunctionBase< TMeasurementVector >
Public Types inherited from itk::FunctionBase< TMeasurementVector, double >
typedef TMeasurementVector	InputType
typedef double	OutputType
Public Types inherited from itk::Object
Public Types inherited from itk::LightObject

Public Member Functions
double	Evaluate (const MeasurementVectorType &measurement) const
virtual const CovarianceMatrixType &	GetCovariance ()
virtual const CovarianceMatrixType &	GetInverseCovariance ()
virtual const MeanVectorType &	GetMean ()
virtual LightObject::Pointer	InternalClone () const
void	SetCovariance (const CovarianceMatrixType &cov)
void	SetMean (const MeanVectorType &mean)
Public Member Functions inherited from itk::Statistics::MembershipFunctionBase< TMeasurementVector >
virtual MeasurementVectorSizeType	GetMeasurementVectorSize () const
virtual void	SetMeasurementVectorSize (MeasurementVectorSizeType s)

Protected Member Functions
	GaussianMembershipFunction (void)
void	PrintSelf (std::ostream &os, Indent indent) const
virtual	~GaussianMembershipFunction (void)
Protected Member Functions inherited from itk::Statistics::MembershipFunctionBase< TMeasurementVector >
	MembershipFunctionBase ()
virtual	~MembershipFunctionBase (void)
Protected Member Functions inherited from itk::FunctionBase< TMeasurementVector, double >
	FunctionBase ()
	~FunctionBase ()
Protected Member Functions inherited from itk::Object
	Object ()
bool	PrintObservers (std::ostream &os, Indent indent) const
virtual void	SetTimeStamp (const TimeStamp &time)
virtual	~Object ()
Protected Member Functions inherited from itk::LightObject
	LightObject ()
virtual void	PrintHeader (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
virtual	~LightObject ()

Private Member Functions
	GaussianMembershipFunction (const Self &)
void	operator= (const Self &)

Private Attributes
CovarianceMatrixType	m_Covariance
bool	m_CovarianceNonsingular
CovarianceMatrixType	m_InverseCovariance
MeanVectorType	m_Mean
double	m_PreFactor
virtual const char *	GetNameOfClass () const
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
static Pointer	New ()

Additional Inherited Members

Detailed Description

template<class TMeasurementVector>
class itk::Statistics::GaussianMembershipFunction< TMeasurementVector >

GaussianMembershipFunction models class membership through a multivariate Gaussian function.

GaussianMembershipFunction is a subclass of MembershipFunctionBase that models class membership (or likelihood) using a multivariate Gaussian function. The mean and covariance structure of the Gaussian are established using the methods SetMean() and SetCovariance(). The mean is a vector-type that is the same vector-type as the measurement vector but guaranteed to have a real element type. For instance, if the measurement type is an Vector<int,3>, then the mean is Vector<double,3>. If the measurement type is a VariableLengthVector<float>, then the mean is VariableLengthVector<double>. In contrast to this behavior, the covariance is always a VariableSizeMatrix<double>.

If the covariance is singular or nearly singular, the membership function behaves somewhat like an impulse located at the mean. In this case, we specify the covariance to be a diagonal matrix with large values along the diagonal. This membership function, therefore, will return small but differentiable values everywher and increase sharply near the mean.

Definition at line 55 of file itkGaussianMembershipFunction.h.