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/*
//
// Copyright 2010-2012 SRI International
//
// This file is part of the Computational Morphometry Toolkit.
//
// http://www.nitrc.org/projects/cmtk/
//
// The Computational Morphometry Toolkit is free software: you can
// redistribute it and/or modify it under the terms of the GNU General Public
// License as published by the Free Software Foundation, either version 3 of
// the License, or (at your option) any later version.
//
// The Computational Morphometry Toolkit is distributed in the hope that it
// will be useful, but WITHOUT ANY WARRANTY; without even the implied
// warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License along
// with the Computational Morphometry Toolkit. If not, see
// <http://www.gnu.org/licenses/>.
//
// $Revision: 5436 $
//
// $LastChangedDate: 2018-12-10 19:01:20 -0800 (Mon, 10 Dec 2018) $
//
// $LastChangedBy: torstenrohlfing $
//
*/
#ifndef __cmtkGaussianKernel_h_included_
#define __cmtkGaussianKernel_h_included_
#include <cmtkconfig.h>
#include <Base/cmtkUnits.h>
#include <Base/cmtkMathUtil.h>
#include <vector>
/** \addtogroup Base */
//@{
namespace
cmtk
{
/// Utility class for generating Gaussian kernels.
template<class TFloat=double>
class GaussianKernel
{
public:
/// This class.
typedef GaussianKernel<TFloat> Self;
/// Get raw kernel value.
static TFloat GetValue( const TFloat x, const TFloat mu, const TFloat sigma )
{
return exp( -MathUtil::Square( (x-mu) / sigma ) / 2 ) / (sqrt(2*M_PI) * sigma);
}
/// Create symmetric kernel.
static std::vector<TFloat> GetSymmetricKernel( const Units::GaussianSigma& sigma /*!< Sigma parameter (standard deviation) of the kernel */,
const TFloat maxError = 1e-5 /*!< Maximum approximation error: the kernel radius is computed so that truncated elements are below this value */ )
{
const TFloat normFactor = static_cast<TFloat>( 1.0/(sqrt(2*M_PI) * sigma.Value()) );
const size_t radius = static_cast<size_t>( Self::GetRadius( sigma, normFactor, maxError ) );
std::vector<TFloat> kernel( 2 * radius + 1 );
for ( size_t i = 0; i <= radius; ++i )
{
kernel[radius-i] = kernel[radius+i] = static_cast<TFloat>( normFactor * exp( -MathUtil::Square( 1.0 * i / sigma.Value() ) / 2 ) );
}
return kernel;
}
/// Create half kernel, starting with center element.
static std::vector<TFloat> GetHalfKernel( const Units::GaussianSigma& sigma /*!< Sigma parameter (standard deviation) of the kernel */,
const TFloat maxError = 1e-5 /*!< Maximum approximation error: the kernel radius is computed so that truncated elements are below this value */ )
{
const double normFactor = 1.0/(sqrt(2*M_PI) * sigma.Value());
const size_t radius = static_cast<size_t>( Self::GetRadius( sigma, normFactor, maxError ) );
std::vector<TFloat> kernel( radius + 1 );
for ( size_t i = 0; i <= radius; ++i )
{
kernel[i] = normFactor * exp( -MathUtil::Square( 1.0 * i / sigma.Value() ) / 2 );
}
return kernel;
}
private:
/// Compute kernel radius based on maximum approximation error and normalization factor.
static TFloat GetRadius( const Units::GaussianSigma& sigma, const TFloat normFactor, const TFloat maxError )
{
if ( maxError >= normFactor ) // if normFactor is less than max error, then we really need no kernel at all
return 0;
else
return static_cast<TFloat>( sqrt( -2.0 * log( maxError / normFactor ) ) * sigma.Value() );
}
};
} // namespace cmtk
//@}
#endif // #ifndef __cmtkGaussianKernel_h_included_
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