itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > Class Template Reference

#include <itkKdTreeBasedKmeansEstimator.h>

Inheritance diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

[legend]Collaboration diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

[legend]List of all members.

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Detailed Description

template<class TKdTree>
class itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >

fast k-means algorithm implementation using k-d tree structure

It returns k mean vectors that are centroids of k-clusters using pre-generated k-d tree. k-d tree generation is done by the WeightedCentroidKdTreeGenerator. The tree construction needs to be done only once. The resulting k-d tree's non-terminal nodes that have their children nodes have vector sums of measurement vectors that belong to the nodes and the number of measurement vectors in addition to the typical node boundary information and pointers to children nodes. Instead of reassigning every measurement vector to the nearest cluster centroid and recalculating centroid, it maintain a set of cluster centroid candidates and using pruning algorithm that utilizes k-d tree, it updates the means of only relevant candidates at each iterations. It would be faster than traditional implementation of k-means algorithm. However, the k-d tree consumes a large amount of memory. The tree construction time and pruning algorithm's performance are important factors to the whole process's performance. If users want to use k-d tree for some purpose other than k-means estimation, they can use the KdTreeGenerator instead of the WeightedCentroidKdTreeGenerator. It will save the tree construction time and memory usage.

Note: There is a second implementation of k-means algorithm in ITK under the While the Kd tree based implementation is more time efficient, the GLA/LBG based algorithm is more memory efficient.

Recent API changes: The static const macro to get the length of a measurement vector, MeasurementVectorSize has been removed to allow the length of a measurement vector to be specified at run time. It is now obtained from the KdTree set as input. You may query this length using the function GetMeasurementVectorSize().

See also:

ImageKmeansModelEstimator

WeightedCentroidKdTreeGenerator, KdTree

Definition at line 67 of file itkKdTreeBasedKmeansEstimator.h.

Public Types
typedef KdTreeNodeType::CentroidType	CentroidType
typedef itk::hash_map< InstanceIdentifier, unsigned int >	ClusterLabelsType
typedef SmartPointer< const Self >	ConstPointer
typedef TKdTree::InstanceIdentifier	InstanceIdentifier
typedef std::vector< ParameterType >	InternalParametersType
typedef TKdTree::KdTreeNodeType	KdTreeNodeType
typedef TKdTree::MeasurementType	MeasurementType
typedef unsigned int	MeasurementVectorSizeType
typedef TKdTree::MeasurementVectorType	MeasurementVectorType
typedef Array< double >	ParametersType
typedef Array< double >	ParameterType
typedef SmartPointer< Self >	Pointer
typedef TKdTree::SampleType	SampleType
typedef KdTreeBasedKmeansEstimator	Self
typedef Object	Superclass
Public Member Functions
virtual LightObject::Pointer	CreateAnother () const
virtual void	DebugOff () const
virtual void	DebugOn () const
virtual void	Delete ()
virtual const double &	GetCentroidPositionChanges ()
virtual const double &	GetCentroidPositionChangesThreshold ()
ClusterLabelsType *	GetClusterLabels ()
Command *	GetCommand (unsigned long tag)
virtual const int &	GetCurrentIteration ()
bool	GetDebug () const
TKdTree *	GetKdTree ()
virtual const int &	GetMaximumIteration ()
virtual const MeasurementVectorSizeType &	GetMeasurementVectorSize ()
const MetaDataDictionary &	GetMetaDataDictionary (void) const
MetaDataDictionary &	GetMetaDataDictionary (void)
virtual unsigned long	GetMTime () const
virtual const char *	GetNameOfClass () const
ParametersType &	GetParameters ()
virtual int	GetReferenceCount () const
bool	HasObserver (const EventObject &event) const
void	InvokeEvent (const EventObject &) const
void	InvokeEvent (const EventObject &)
virtual void	Modified () const
void	Print (std::ostream &os, Indent indent=0) const
virtual void	Register () const
void	RemoveAllObservers ()
void	RemoveObserver (unsigned long tag)
Set Get the termination threshold for the squared sum *of changes in centroid postions after one iteration *virtual void	SetCentroidPositionChangesThreshold (double _arg)
void	SetDebug (bool debugFlag) const
Set Get the pointer to the KdTree *void	SetKdTree (TKdTree *tree)
Set Get maximum iteration limit *virtual void	SetMaximumIteration (int _arg)
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetParameters (ParametersType &params)
virtual void	SetReferenceCount (int)
void	SetUseClusterLabels (bool flag)
void	StartOptimization ()
virtual void	UnRegister () const
Static Public Member Functions
static void	BreakOnError ()
static bool	GetGlobalWarningDisplay ()
static void	GlobalWarningDisplayOff ()
static void	GlobalWarningDisplayOn ()
static Pointer	New ()
This is a global flag that controls whether any warning *or error messages are displayed *static void	SetGlobalWarningDisplay (bool flag)
Public Attributes
Allow people to add remove invoke observers(callbacks) to any ITK *object.This is an implementation of the subject/observer design *pattern.An observer is added by specifying an event to respond to *and an itk unsigned lon	AddObserver )(const EventObject &event, Command *) const
This is a global flag that controls whether any	debug
Protected Member Functions
void	CopyParameters (InternalParametersType &source, ParametersType &target)
void	CopyParameters (ParametersType &source, InternalParametersType &target)
void	CopyParameters (InternalParametersType &source, InternalParametersType &target)
void	FillClusterLabels (KdTreeNodeType *node, int closestIndex)
void	Filter (KdTreeNodeType *node, std::vector< int > validIndexes, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
int	GetClosestCandidate (ParameterType &measurements, std::vector< int > &validIndexes)
imports the measurements measurement vector data to the point *void	GetPoint (ParameterType &point, MeasurementVectorType measurements)
double	GetSumOfSquaredPositionChanges (InternalParametersType &previous, InternalParametersType &current)
bool	IsFarther (ParameterType &pointA, ParameterType &pointB, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
	KdTreeBasedKmeansEstimator ()
bool	PrintObservers (std::ostream &os, Indent indent) const
void	PrintPoint (ParameterType &point)
void	PrintSelf (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
virtual	~KdTreeBasedKmeansEstimator ()
Protected Attributes
int	m_ReferenceCount
SimpleFastMutexLock	m_ReferenceCountLock
Methods invoked by virtual Print() to print information about the object *including superclasses.Typically not called by the user(use Print()*instead) but used in the hierarchical print process to combine the *output of several classes.*/virtual void PrintSelf(std voi	PrintHeader )(std::ostream &os, Indent indent) const
Classes
class	CandidateVector

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Member Typedef Documentation

template<class TKdTree> typedef KdTreeNodeType::CentroidType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CentroidType

Definition at line 89 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef itk::hash_map< InstanceIdentifier, unsigned int > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ClusterLabelsType

Definition at line 145 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef SmartPointer<const Self> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ConstPointer

Reimplemented from itk::Object. Definition at line 75 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef TKdTree::InstanceIdentifier itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InstanceIdentifier

Definition at line 87 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef std::vector< ParameterType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InternalParametersType

Definition at line 98 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef TKdTree::KdTreeNodeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeNodeType

Types for the KdTree data structure Definition at line 81 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef TKdTree::MeasurementType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementType

Definition at line 85 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef unsigned int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorSizeType

Typedef for the length of a measurement vector Definition at line 93 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef TKdTree::MeasurementVectorType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorType

Definition at line 86 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParametersType

Definition at line 99 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParameterType

Parameters type. It defines a position in the optimization search space. Definition at line 97 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef SmartPointer<Self> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Pointer

Reimplemented from itk::Object. Definition at line 74 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef TKdTree::SampleType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SampleType

Definition at line 88 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef KdTreeBasedKmeansEstimator itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Self

Standard "Self" typedef. Reimplemented from itk::Object. Definition at line 72 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> typedef Object itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Superclass

Reimplemented from itk::Object. Definition at line 73 of file itkKdTreeBasedKmeansEstimator.h.

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Constructor & Destructor Documentation

template<class TKdTree> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeBasedKmeansEstimator (   )  [protected]

template<class TKdTree> virtual itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::~KdTreeBasedKmeansEstimator (   )  [inline, protected, virtual]

Definition at line 155 of file itkKdTreeBasedKmeansEstimator.h.

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Member Function Documentation

static void itk::LightObject::BreakOnError (   )  [static, inherited]

This method is called when itkExceptionMacro executes. It allows the debugger to break on error.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters (  InternalParametersType &  source, ParametersType &  target )  [protected]

copies the source parameters (k-means) to the target

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters (  ParametersType &  source, InternalParametersType &  target )  [protected]

copies the source parameters (k-means) to the target

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters (  InternalParametersType &  source, InternalParametersType &  target )  [protected]

copies the source parameters (k-means) to the target

virtual LightObject::Pointer itk::Object::CreateAnother (   )  const [virtual, inherited]

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class. Reimplemented from itk::LightObject.

virtual void itk::Object::DebugOff (   )  const [virtual, inherited]

Turn debugging output off.

virtual void itk::Object::DebugOn (   )  const [virtual, inherited]

Turn debugging output on.

virtual void itk::LightObject::Delete (   )  [virtual, inherited]

Delete an itk object. This method should always be used to delete an object when the new operator was used to create it. Using the C delete method will not work with reference counting.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::FillClusterLabels (  KdTreeNodeType *  node, int  closestIndex )  [protected]

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Filter (  KdTreeNodeType *  node, std::vector< int >  validIndexes, MeasurementVectorType &  lowerBound, MeasurementVectorType &  upperBound )  [protected]

recursive pruning algorithm. the "validIndexes" vector contains only the indexes of the surviving candidates for the "node"

template<class TKdTree> virtual const double& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChanges (   )  [virtual]

template<class TKdTree> virtual const double& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChangesThreshold (   )  [virtual]

template<class TKdTree> int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClosestCandidate (  ParameterType &  measurements, std::vector< int > &  validIndexes )  [protected]

get the index of the closest candidate to the "measurements" measurement vector

template<class TKdTree> ClusterLabelsType* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClusterLabels (   )  [inline]

Definition at line 150 of file itkKdTreeBasedKmeansEstimator.h.

Command* itk::Object::GetCommand (  unsigned long  tag  )  [inherited]

Get the command associated with the given tag. NOTE: This returns a pointer to a Command, but it is safe to asign this to a Command::Pointer. Since Command inherits from LightObject, at this point in the code, only a pointer or a reference to the Command can be used.

template<class TKdTree> virtual const int& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration (   )  [virtual]

bool itk::Object::GetDebug (   )  const [inherited]

Get the value of the debug flag.

static bool itk::Object::GetGlobalWarningDisplay (   )  [static, inherited]

template<class TKdTree> TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree (   )  [inline]

Definition at line 130 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual const int& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration (   )  [virtual]

template<class TKdTree> virtual const MeasurementVectorSizeType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMeasurementVectorSize (   )  [virtual]

Get the length of measurement vectors in the KdTree

const MetaDataDictionary& itk::Object::GetMetaDataDictionary (  void   )  const [inherited]

Returns: A constant reference to this objects MetaDataDictionary.

MetaDataDictionary& itk::Object::GetMetaDataDictionary (  void   )  [inherited]

Returns: A reference to this objects MetaDataDictionary. Warning: This reference may be changed.

virtual unsigned long itk::Object::GetMTime (   )  const [virtual, inherited]

Return this objects modified time. Reimplemented in itk::ImageRegistrationMethod< TFixedImage, TMovingImage >, itk::DeformationFieldSource< TOutputImage >, itk::InverseDeformationFieldImageFilter< TInputImage, TOutputImage >, itk::ResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::VectorResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::BoundingBox< TPointIdentifier, VPointDimension, TCoordRep, TPointsContainer >, itk::SceneSpatialObject< SpaceDimension >, and itk::SceneSpatialObject< NDimensions >.

template<class TKdTree> virtual const char* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetNameOfClass (   )  const [virtual]

Run-time type information (and related methods). Reimplemented from itk::Object.

template<class TKdTree> ParametersType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters (  void   )  [inline]

Get current position of the optimization. Definition at line 106 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> imports the measurements measurement vector data to the point* void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetPoint (  ParameterType &  point, MeasurementVectorType  measurements )  [inline, protected]

Definition at line 279 of file itkKdTreeBasedKmeansEstimator.h.

virtual int itk::LightObject::GetReferenceCount (   )  const [inline, virtual, inherited]

Gets the reference count on this object. Definition at line 98 of file itkLightObject.h.

template<class TKdTree> double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetSumOfSquaredPositionChanges (  InternalParametersType &  previous, InternalParametersType &  current )  [protected]

gets the sum of squared difference between the previous position and current postion of all centroid. This is the primary termination condition for this algorithm. If the return value is less than the value that was set by the SetCentroidPositionChangesThreshold method.

static void itk::Object::GlobalWarningDisplayOff (   )  [inline, static, inherited]

Definition at line 100 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay().

static void itk::Object::GlobalWarningDisplayOn (   )  [inline, static, inherited]

Definition at line 98 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay().

bool itk::Object::HasObserver (  const EventObject &  event  )  const [inherited]

Return true if an observer is registered for this event.

void itk::Object::InvokeEvent (  const EventObject &   )  const [inherited]

Call Execute on all the Commands observing this event id. The actions triggered by this call doesn't modify this object.

void itk::Object::InvokeEvent (  const EventObject &   )  [inherited]

Call Execute on all the Commands observing this event id.

template<class TKdTree> bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::IsFarther (  ParameterType &  pointA, ParameterType &  pointB, MeasurementVectorType &  lowerBound, MeasurementVectorType &  upperBound )  [protected]

returns true if the "pointA is farther than pointB to the boundary

virtual void itk::Object::Modified (   )  const [virtual, inherited]

Update the modification time for this object. Many filters rely on the modification time to determine if they need to recompute their data. Referenced by itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetCenter(), itk::HistogramAlgorithmBase< TInputHistogram >::SetInputHistogram(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetMatrix(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetOffset(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetRealThresholds(), itk::CollidingFrontsImageFilter< TInputImage, TOutputImage >::SetSeedPoints1(), itk::CollidingFrontsImageFilter< TInputImage, TOutputImage >::SetSeedPoints2(), itk::NonUniformBSpline< TDimension >::SetSplineOrder(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetThresholds(), itk::Statistics::GoodnessOfFitFunctionBase< typename ComponentType::HistogramType >::SetTotalObservedScale(), and itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetTranslation().

template<class TKdTree> static Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::New (   )  [static]

Method for creation through the object factory. Reimplemented from itk::Object.

void itk::LightObject::Print (  std::ostream &  os, Indent  indent = 0 )  const [inherited]

Cause the object to print itself out.

bool itk::Object::PrintObservers (  std::ostream &  os, Indent  indent )  const [protected, inherited]

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint (  ParameterType &  point  )  [inline, protected]

Definition at line 289 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintSelf (  std::ostream &  os, Indent  indent )  const [protected, virtual]

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes. Reimplemented from itk::Object.

virtual void itk::LightObject::PrintTrailer (  std::ostream &  os, Indent  indent )  const [protected, virtual, inherited]

virtual void itk::Object::Register (   )  const [virtual, inherited]

Increase the reference count (mark as used by another object). Reimplemented from itk::LightObject.

void itk::Object::RemoveAllObservers (   )  [inherited]

Remove all observers .

void itk::Object::RemoveObserver (  unsigned long  tag  )  [inherited]

Remove the observer with this tag value.

template<class TKdTree> Set Get the termination threshold for the squared sum* of changes in centroid postions after one iteration* virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetCentroidPositionChangesThreshold (  double  _arg  )  [virtual]

void itk::Object::SetDebug (  bool  debugFlag  )  const [inherited]

Set the value of the debug flag. A non-zero value turns debugging on.

This is a global flag that controls whether any warning* or error messages are displayed* static void itk::Object::SetGlobalWarningDisplay (  bool  flag  )  [static, inherited]

Referenced by itk::Object::GlobalWarningDisplayOff(), and itk::Object::GlobalWarningDisplayOn().

template<class TKdTree> Set Get the pointer to the KdTree* void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetKdTree (  TKdTree *  tree  )  [inline]

Definition at line 121 of file itkKdTreeBasedKmeansEstimator.h. References itk::MeasurementVectorTraits::SetLength().

template<class TKdTree> Set Get maximum iteration limit* virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration (  int  _arg  )  [virtual]

void itk::Object::SetMetaDataDictionary (  const MetaDataDictionary &  rhs  )  [inherited]

Returns: Set the MetaDataDictionary

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters (  ParametersType &  params  )  [inline]

Set the position to initialize the optimization. Definition at line 102 of file itkKdTreeBasedKmeansEstimator.h.

virtual void itk::Object::SetReferenceCount (  int   )  [virtual, inherited]

Sets the reference count (use with care) Reimplemented from itk::LightObject.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels (  bool  flag  )  [inline]

Definition at line 147 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::StartOptimization (   )

Start optimization Optimization will stop when it meets either of two termination conditions, the maximum iteration limit or epsilon (minimal changes in squared sum of changes in centroid positions)

virtual void itk::Object::UnRegister (   )  const [virtual, inherited]

Decrease the reference count (release by another object). Reimplemented from itk::LightObject.

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Member Data Documentation

Allow people to add remove invoke observers (callbacks) to any ITK * object. This is an implementation of the subject/observer design * pattern. An observer is added by specifying an event to respond to * and an itk unsigned lon itk::Object::AddObserver)(const EventObject &event, Command *) const [inherited]

This is a global flag that controls whether any itk::Object::debug [inherited]

Definition at line 94 of file itkObject.h.

int itk::LightObject::m_ReferenceCount [mutable, protected, inherited]

Number of uses of this object by other objects. Definition at line 119 of file itkLightObject.h.

SimpleFastMutexLock itk::LightObject::m_ReferenceCountLock [mutable, protected, inherited]

Mutex lock to protect modification to the reference count Definition at line 122 of file itkLightObject.h.

Methods invoked by virtual Print () to print information about the object * including superclasses. Typically not called by the user (use Print() * instead) but used in the hierarchical print process to combine the * output of several classes. */ virtual void PrintSelf(std voi itk::LightObject::PrintHeader)(std::ostream &os, Indent indent) const [protected, inherited]

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

• itkKdTreeBasedKmeansEstimator.h

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