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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkSimpleFuzzyConnectednessRGBImageFilter.txx
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkSimpleFuzzyConnectednessRGBImageFilter_txx
#define __itkSimpleFuzzyConnectednessRGBImageFilter_txx
#include "itkSimpleFuzzyConnectednessRGBImageFilter.h"
#include "vnl/vnl_math.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "itkNumericTraits.h"
namespace itk {
template <class TInputImage, class TOutputImage>
SimpleFuzzyConnectednessRGBImageFilter<TInputImage,TOutputImage>
::SimpleFuzzyConnectednessRGBImageFilter()
{
}
template <class TInputImage, class TOutputImage>
SimpleFuzzyConnectednessRGBImageFilter<TInputImage,TOutputImage>
::~SimpleFuzzyConnectednessRGBImageFilter()
{
}
template <class TInputImage, class TOutputImage>
double
SimpleFuzzyConnectednessRGBImageFilter<TInputImage,TOutputImage>
::FuzzyAffinity(const PixelType f1, const PixelType f2)
{
double save[3];
save[0] = 0.5 * (f1[0]+f2[0]) - m_Mean[0];
save[1] = 0.5 * (f1[1]+f2[1]) - m_Mean[1];
save[2] = 0.5 * (f1[2]+f2[2]) - m_Mean[2];
double s00 = save[0]*save[0];
double s01 = save[0]*save[1];
double s02 = save[0]*save[2];
double s11 = save[1]*save[1];
double s12 = save[1]*save[2];
double s22 = save[2]*save[2];
double tmp1 = s00*(m_VarianceInverse[0][0])
+ s11*(m_VarianceInverse[1][1])
+ s22*(m_VarianceInverse[2][2])
+ s01*(m_VarianceInverse[0][1]+m_VarianceInverse[1][0])
+ s02*(m_VarianceInverse[0][2]+m_VarianceInverse[2][0])
+ s12*(m_VarianceInverse[1][2]+m_VarianceInverse[2][1]);
if(this->GetWeight() == 1)
{
return( (NumericTraits<unsigned short>::max())*(vcl_exp(-0.5*tmp1)) );
}
else
{
save[0] = f1[0]-f2[0];
save[1] = f1[1]-f2[1];
save[2] = f1[2]-f2[2];
if(save[0] < 0)
save[0]=-save[0];
if(save[1] < 0)
save[1]=-save[1];
if(save[2] < 0)
save[2]=-save[2];
save[0] = save[0] - m_Diff_Mean[0];
save[1] = save[1] - m_Diff_Mean[1];
save[2] = save[2] - m_Diff_Mean[2];
s00 = save[0]*save[0];
s01 = save[0]*save[1];
s02 = save[0]*save[2];
s11 = save[1]*save[1];
s12 = save[1]*save[2];
s22 = save[2]*save[2];
double tmp3 = s00*(m_Diff_VarianceInverse[0][0])
+ s11*(m_Diff_VarianceInverse[1][1])
+ s22*(m_Diff_VarianceInverse[2][2])
+ s01*(m_Diff_VarianceInverse[0][1]+m_Diff_VarianceInverse[1][0])
+ s02*(m_Diff_VarianceInverse[0][2]+m_Diff_VarianceInverse[2][0])
+ s12*(m_Diff_VarianceInverse[1][2]+m_Diff_VarianceInverse[2][1]);
return( (NumericTraits<unsigned short>::max())*(this->GetWeight()*vcl_exp(-0.5*tmp1)
+(1-this->GetWeight())*vcl_exp(-0.5*tmp3)) );
}
}
template <class TInputImage, class TOutputImage>
void
SimpleFuzzyConnectednessRGBImageFilter<TInputImage,TOutputImage>
::GenerateData()
{
/* Compute the inverse of the Varianceiance Matrices. */
m_VarianceDet = m_Variance[0][0]*m_Variance[1][1]*m_Variance[2][2]
+m_Variance[1][0]*m_Variance[2][1]*m_Variance[0][2]
+m_Variance[0][1]*m_Variance[1][2]*m_Variance[2][0]
-m_Variance[2][0]*m_Variance[1][1]*m_Variance[0][2]
-m_Variance[0][1]*m_Variance[1][0]*m_Variance[2][2]
-m_Variance[0][0]*m_Variance[1][2]*m_Variance[2][1];
m_VarianceInverse[0][0]=(m_Variance[1][1]*m_Variance[2][2]-m_Variance[2][1]*m_Variance[1][2])
/m_VarianceDet;
m_VarianceInverse[0][1]=-(m_Variance[1][0]*m_Variance[2][2]-m_Variance[2][0]*m_Variance[1][2])
/m_VarianceDet;
m_VarianceInverse[0][2]=(m_Variance[1][0]*m_Variance[2][1]-m_Variance[2][0]*m_Variance[1][1])
/m_VarianceDet;
m_VarianceInverse[1][0]=-(m_Variance[0][1]*m_Variance[2][2]-m_Variance[2][1]*m_Variance[0][2])
/m_VarianceDet;
m_VarianceInverse[1][1]=(m_Variance[0][0]*m_Variance[2][2]-m_Variance[2][0]*m_Variance[0][2])
/m_VarianceDet;
m_VarianceInverse[1][2]=-(m_Variance[0][0]*m_Variance[2][1]-m_Variance[2][0]*m_Variance[0][1])
/m_VarianceDet;
m_VarianceInverse[2][0]=(m_Variance[0][1]*m_Variance[1][2]-m_Variance[1][1]*m_Variance[0][2])
/m_VarianceDet;
m_VarianceInverse[2][1]=-(m_Variance[0][0]*m_Variance[1][2]-m_Variance[1][0]*m_Variance[0][2])
/m_VarianceDet;
m_VarianceInverse[2][2]=(m_Variance[0][0]*m_Variance[1][1]-m_Variance[1][0]*m_Variance[0][1])
/m_VarianceDet;
if((int)(this->GetWeight()*100+0.5) > 1)
{
//need to use the difference information.
m_Diff_VarianceDet = m_Diff_Variance[0][0]*m_Diff_Variance[1][1]*m_Diff_Variance[2][2]
+m_Diff_Variance[1][0]*m_Diff_Variance[2][1]*m_Diff_Variance[0][2]
+m_Diff_Variance[0][1]*m_Diff_Variance[1][2]*m_Diff_Variance[2][0]
-m_Diff_Variance[2][0]*m_Diff_Variance[1][1]*m_Diff_Variance[0][2]
-m_Diff_Variance[0][1]*m_Diff_Variance[1][0]*m_Diff_Variance[2][2]
-m_Diff_Variance[0][0]*m_Diff_Variance[1][2]*m_Diff_Variance[2][1];
m_Diff_VarianceInverse[0][0]=(m_Diff_Variance[1][1]*m_Diff_Variance[2][2]-m_Diff_Variance[2][1]*m_Diff_Variance[1][2])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[0][1]=-(m_Diff_Variance[1][0]*m_Diff_Variance[2][2]-m_Diff_Variance[2][0]*m_Diff_Variance[1][2])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[0][2]=(m_Diff_Variance[1][0]*m_Diff_Variance[2][1]-m_Diff_Variance[2][0]*m_Diff_Variance[1][1])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[1][0]=-(m_Diff_Variance[0][1]*m_Diff_Variance[2][2]-m_Diff_Variance[2][1]*m_Diff_Variance[0][2])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[1][1]=(m_Diff_Variance[0][0]*m_Diff_Variance[2][2]-m_Diff_Variance[2][0]*m_Diff_Variance[0][2])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[1][2]=-(m_Diff_Variance[0][0]*m_Diff_Variance[2][1]-m_Diff_Variance[2][0]*m_Diff_Variance[0][1])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[2][0]=(m_Diff_Variance[0][1]*m_Diff_Variance[1][2]-m_Diff_Variance[1][1]*m_Diff_Variance[0][2])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[2][1]=-(m_Diff_Variance[0][0]*m_Diff_Variance[1][2]-m_Diff_Variance[1][0]*m_Diff_Variance[0][2])
/m_Diff_VarianceDet;
m_Diff_VarianceInverse[2][2]=(m_Diff_Variance[0][0]*m_Diff_Variance[1][1]-m_Diff_Variance[1][0]*m_Diff_Variance[0][1])
/m_Diff_VarianceDet;
}
Superclass::GenerateData();
}
template <class TInputImage, class TOutputImage>
void
SimpleFuzzyConnectednessRGBImageFilter<TInputImage,TOutputImage>
::PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Mean = " << m_Mean << std::endl;
os << indent << "Diff_Mean = " << m_Diff_Mean << std::endl;
}
} /* end namespace itk. */
#endif
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