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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
*
* Unless required by applicable law or agreed to in writing, software
* 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 itkNormalizedCorrelationPointSetToImageMetric_h
#define itkNormalizedCorrelationPointSetToImageMetric_h
#include "itkPointSetToImageMetric.h"
#include "itkCovariantVector.h"
#include "itkPoint.h"
namespace itk
{
/** \class NormalizedCorrelationPointSetToImageMetric
* \brief Computes similarity between pixel values of a point set and
* intensity values of an image.
*
* This metric computes the correlation between point values in the fixed
* point-set and pixel values in the moving image. The correlation is
* normalized by the autocorrelation values of both the point-set and the
* moving image. The spatial correspondence between the point-set and the image
* is established through a Transform. Pixel values are taken from the fixed
* point-set. Their positions are mapped to the moving image and result in
* general in non-grid position on it. Values at these non-grid position of
* the moving image are interpolated using a user-selected Interpolator.
*
* \ingroup RegistrationMetrics
* \ingroup ITKRegistrationCommon
*/
template< typename TFixedPointSet, typename TMovingImage >
class ITK_TEMPLATE_EXPORT NormalizedCorrelationPointSetToImageMetric:
public PointSetToImageMetric< TFixedPointSet, TMovingImage >
{
public:
/** Standard class typedefs. */
typedef NormalizedCorrelationPointSetToImageMetric Self;
typedef PointSetToImageMetric< TFixedPointSet, TMovingImage > Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(NormalizedCorrelationPointSetToImageMetric,
PointSetToImageMetric);
/** Types transferred from the base class */
typedef typename Superclass::RealType RealType;
typedef typename Superclass::TransformType TransformType;
typedef typename Superclass::TransformPointer TransformPointer;
typedef typename Superclass::TransformParametersType TransformParametersType;
typedef typename Superclass::TransformJacobianType TransformJacobianType;
typedef typename Superclass::GradientPixelType GradientPixelType;
typedef typename Superclass::MeasureType MeasureType;
typedef typename Superclass::DerivativeType DerivativeType;
typedef typename Superclass::FixedPointSetType FixedPointSetType;
typedef typename Superclass::MovingImageType MovingImageType;
typedef typename Superclass::FixedPointSetConstPointer FixedPointSetConstPointer;
typedef typename Superclass::MovingImageConstPointer MovingImageConstPointer;
typedef typename Superclass::PointIterator PointIterator;
typedef typename Superclass::PointDataIterator PointDataIterator;
typedef typename Superclass::InputPointType InputPointType;
typedef typename Superclass::OutputPointType OutputPointType;
/** Get the derivatives of the match measure. */
void GetDerivative(const TransformParametersType & parameters,
DerivativeType & Derivative) const ITK_OVERRIDE;
/** Get the value for single valued optimizers. */
MeasureType GetValue(const TransformParametersType & parameters) const ITK_OVERRIDE;
/** Get value and derivatives for multiple valued optimizers. */
void GetValueAndDerivative(const TransformParametersType & parameters,
MeasureType & Value, DerivativeType & Derivative) const ITK_OVERRIDE;
/** Set/Get SubtractMean boolean. If true, the sample mean is subtracted
* from the sample values in the cross-correlation formula and
* typically results in narrower valleys in the cost function.
* Default value is false. */
itkSetMacro(SubtractMean, bool);
itkGetConstReferenceMacro(SubtractMean, bool);
itkBooleanMacro(SubtractMean);
protected:
NormalizedCorrelationPointSetToImageMetric();
virtual ~NormalizedCorrelationPointSetToImageMetric() ITK_OVERRIDE {}
void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
private:
ITK_DISALLOW_COPY_AND_ASSIGN(NormalizedCorrelationPointSetToImageMetric);
bool m_SubtractMean;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkNormalizedCorrelationPointSetToImageMetric.hxx"
#endif
#endif
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