#include <itkGaussianDerivativeOperator.h>

Inheritance diagram for itk::GaussianDerivativeOperator< TPixel, VDimension, TAllocator >:

Collaboration diagram for itk::GaussianDerivativeOperator< TPixel, VDimension, TAllocator >:

Public Types
typedef DerivativeOperator < TPixel, VDimension, TAllocator >	DerivativeOperatorType
typedef GaussianOperator < TPixel, VDimension, TAllocator >	GaussianOperatorType
typedef GaussianDerivativeOperator	Self
typedef NeighborhoodOperator < TPixel, VDimension, TAllocator >	Superclass
Public Member Functions
	GaussianDerivativeOperator ()
	GaussianDerivativeOperator (const Self &other)
double	GetMaximumError ()
Self &	operator= (const Self &other)
virtual void	PrintSelf (std::ostream &os, Indent i) const
void	SetMaximumError (const double maxerror)
void	SetMaximumKernelWidth (unsigned int n)

void	SetNormalizeAcrossScale (bool flag)
bool	GetNormalizeAcrossScale () const
virtual void	NormalizeAcrossScaleOn ()
virtual void	NormalizeAcrossScaleOff ()

void	SetVariance (const double variance)
double	GetVariance () const

void	SetSpacing (const double spacing)
double	GetSpacing () const

void	SetOrder (const unsigned int order)
unsigned int	GetOrder () const
Protected Types
typedef Superclass::CoefficientVector	CoefficientVector
Protected Member Functions
void	Fill (const CoefficientVector &coeff)
CoefficientVector	GenerateCoefficients ()
Static Protected Member Functions
static double	ModifiedBesselI (int, double)
static double	ModifiedBesselI0 (double)
static double	ModifiedBesselI1 (double)
Private Member Functions
CoefficientVector	GenerateGaussianCoefficients () const
const char *	GetNameOfClass () const
Private Attributes
double	m_MaximumError
unsigned int	m_MaximumKernelWidth
bool	m_NormalizeAcrossScale
unsigned int	m_Order
double	m_Spacing
double	m_Variance

Detailed Description

template<class TPixel, unsigned int VDimension = 2, class TAllocator = NeighborhoodAllocator< TPixel >>
class itk::GaussianDerivativeOperator< TPixel, VDimension, TAllocator >

A NeighborhoodOperator whose coefficients are a one dimensional, discrete derivative Gaussian kernel.

GaussianDerivativeOperator can be used to calculate Gaussian derivatives by taking its inner product with to a Neighborhood (NeighborhooIterator) that is swept across an image region. It is a directional operator. N successive applications oriented along each dimensional direction will calculate separable, efficient, N-D Gaussian derivatives of an image region.

GaussianDerivativeOperator takes three parameters:

(1) The floating-point variance of the desired Gaussian function.

(2) The order of the derivative to be calculated (zero order means it performs only smoothing as a standard itk::GaussianOperator)

(3) The "maximum error" allowed in the discrete Gaussian function. "Maximum errror" is defined as the difference between the area under the discrete Gaussian curve and the area under the continuous Gaussian. Maximum error affects the Gaussian operator size. Care should be taken not to make this value too small relative to the variance lest the operator size become unreasonably large.

References: The Gaussian kernel contained in this operator was described by Tony Lindeberg (Discrete Scale-Space Theory and the Scale-Space Primal Sketch. Dissertation. Royal Institute of Technology, Stockholm, Sweden. May 1991.).

Author:: Ivan Macia, VICOMTech, Spain, http://www.vicomtech.es

This implementation is derived from the Insight Journal paper: http://hdl.handle.net/1926/1290

See also:: GaussianOperator; NeighborhoodOperator; NeighborhoodIterator; Neighborhood

Wiki Examples:

All Examples

Create a Gaussian derivative kernel

Definition at line 77 of file itkGaussianDerivativeOperator.h.