libinsighttoolkit4-dev 4.9.0-4ubuntu1 / usr / include / ITK-4.9 / itkManifoldParzenWindowsPointSetFunction.h

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 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
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 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef itkManifoldParzenWindowsPointSetFunction_h
#define itkManifoldParzenWindowsPointSetFunction_h

#include "itkPointSetFunction.h"

#include "itkGaussianMembershipFunction.h"
#include "itkMatrix.h"
#include "itkPointSet.h"
#include "itkPointsLocator.h"
#include "itkVector.h"

#include <vector>

namespace itk
{

/** \class ManifoldParzenWindowsPointSetFunction
 * \brief Point set function based on n-dimensional parzen windowing.
 *
 * This class allows evaluating a function derived from a point set
 * by creating a continuous distribution using manifold parzen windowing.
 * Each point is associated with a Gaussian and local shape can
 * be encoded in the covariance matrix.
 *
 * \ingroup ITKMetricsv4
 */
template <typename TPointSet, typename TOutput = double, typename TCoordRep = double>
class ManifoldParzenWindowsPointSetFunction
: public PointSetFunction<TPointSet, TOutput, TCoordRep>
{
public:
  typedef ManifoldParzenWindowsPointSetFunction            Self;
  typedef PointSetFunction<TPointSet, TOutput, TCoordRep>  Superclass;
  typedef SmartPointer<Self>                               Pointer;
  typedef SmartPointer<const Self>                         ConstPointer;

  /** Method for creation through the object factory. */
  itkNewMacro(Self);

  /** Extract dimension from output image. */
  itkStaticConstMacro( PointDimension, unsigned int, TPointSet::PointDimension );

  typedef typename Superclass::InputPointSetType   InputPointSetType;
  typedef typename Superclass::InputPointType      InputPointType;

  /** Point set typedef support. */
  typedef TPointSet                                        PointSetType;
  typedef typename PointSetType::PointType                 PointType;
  typedef typename PointSetType::PointsContainer           PointsContainer;
  typedef typename PointsContainer::ElementIdentifier      PointIdentifier;

  /** Other typedef */
  typedef TOutput                                          RealType;
  typedef TOutput                                          OutputType;
  typedef TCoordRep                                        CoordRepType;

  /** Typedef for points locator class to speed up finding neighboring points */
  typedef PointsLocator< PointsContainer>                       PointsLocatorType;
  typedef typename PointsLocatorType::NeighborsIdentifierType   NeighborsIdentifierType;

  typedef typename Statistics::
    GaussianMembershipFunction<PointType>                  GaussianType;
  typedef typename GaussianType::Pointer                   GaussianPointer;
  typedef typename GaussianType::ConstPointer              GaussianConstPointer;
  typedef std::vector<GaussianPointer>                     GaussianContainerType;
  typedef typename GaussianType::CovarianceMatrixType      CovarianceMatrixType;

  /** Helper functions */

  /**
   * Set the covariance K neighborhood.  For a given point the closest K
   * points are used to construct the corresponding covariance reflecting
   * the local point set structure.  Default = 5.
   */
  itkSetMacro( CovarianceKNeighborhood, unsigned int );

  /** Get the covariance k neighborhood size.  Default = 5.*/
  itkGetConstMacro( CovarianceKNeighborhood, unsigned int );

  /**
   * Set the evaluation K neighborhood.  To evaluate the the manifold parzen
   * windows function, one could sum the value contributed by each Gaussian or
   * to speed calculation, we could sum the value contributed by the nearest
   * K Gaussians.  Default = 50.
   */
  itkSetMacro( EvaluationKNeighborhood, unsigned int );

  /** Get the evaluation K neighborhood.  Default = 50.*/
  itkGetConstMacro( EvaluationKNeighborhood, unsigned int );

  /**
   * Set the regularization sigma.  To avoid singular covariance matrices,
   * a regularization sigma value is added to the diagonal.  Default = 1.0.
   */
  itkSetMacro( RegularizationSigma, RealType );

  /** Get the regularization sigma.  Default = 1.0. */
  itkGetConstMacro( RegularizationSigma, RealType );

  /**
   * Set the kernel sigma.  In constructing the covariance from k neighbors,
   * a Gaussian is used to weight more strongly the closest neighbors.  This
   * defines that weighting Gaussian.  Default = 1.0.
   */
  itkSetMacro( KernelSigma, RealType );

  /** Get the kernel sigma.  Default = 1.0. */
  itkGetConstMacro( KernelSigma, RealType );

  /**
   * Normalize covariance by the sum of the weights of the nearest neighbors.
   * Default = true.
   */
  itkSetMacro( Normalize, bool );

  /**
   * Normalize covariance by the sum of the weights of the nearest neighbors.
   * Default = true.
   */
  itkGetConstMacro( Normalize, bool );

  /**
   * Normalize covariance by the sum of the weights of the nearest neighbors.
   * Default = true.
   */
  itkBooleanMacro( Normalize );

  /**
   * Construct covariances using the local neighborhood point set structure.
   * Otherwise, the Gaussian for each point is characterized by the value
   * of m_RegularizationSigma.  Default = true.
   */
  itkSetMacro( UseAnisotropicCovariances, bool );

  /**
   * Construct covariances using the local neighborhood point set structure.
   * Otherwise, the Gaussian for each point is characterized by the value
   * of m_RegularizationSigma.  Default = true.
   */
  itkGetConstMacro( UseAnisotropicCovariances, bool );

  /**
   * Construct covariances using the local neighborhood point set structure.
   * Otherwise, the Gaussian for each point is characterized by the value
   * of m_RegularizationSigma.  Default = true.
   */
  itkBooleanMacro( UseAnisotropicCovariances );

  /** Set the input point set */
  virtual void SetInputPointSet( const InputPointSetType * ) ITK_OVERRIDE;

  /** Evaluate function value at specified point */
  virtual TOutput Evaluate( const InputPointType & ) const ITK_OVERRIDE;

  /** Get Gaussian corresponding to a specific point */
  GaussianConstPointer GetGaussian( PointIdentifier ) const;

  /** Get the points locator describing the point set neighborhood */
  itkGetModifiableObjectMacro(PointsLocator, PointsLocatorType );

protected:
  ManifoldParzenWindowsPointSetFunction();
  virtual ~ManifoldParzenWindowsPointSetFunction();
  void PrintSelf( std::ostream& os, Indent indent ) const ITK_OVERRIDE;

  void GenerateData();

private:
  ManifoldParzenWindowsPointSetFunction( const Self& ) ITK_DELETE_FUNCTION;
  void operator=( const Self& ) ITK_DELETE_FUNCTION;

  typename PointsLocatorType::Pointer           m_PointsLocator;

  unsigned int                                  m_CovarianceKNeighborhood;
  unsigned int                                  m_EvaluationKNeighborhood;
  RealType                                      m_RegularizationSigma;
  RealType                                      m_KernelSigma;

  GaussianContainerType                         m_Gaussians;
  bool                                          m_Normalize;
  bool                                          m_UseAnisotropicCovariances;
};

} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkManifoldParzenWindowsPointSetFunction.hxx"
#endif

#endif