/************************************************************************/
/*                                                                      */
/*               Copyright 1998-2002 by Ullrich Koethe                  */
/*       Cognitive Systems Group, University of Hamburg, Germany        */
/*                                                                      */
/*    This file is part of the VIGRA computer vision library.           */
/*    ( Version 1.3.2, Jan 27 2005 )                                    */
/*    You may use, modify, and distribute this software according       */
/*    to the terms stated in the LICENSE file included in               */
/*    the VIGRA distribution.                                           */
/*                                                                      */
/*    The VIGRA Website is                                              */
/*        http://kogs-www.informatik.uni-hamburg.de/~koethe/vigra/      */
/*    Please direct questions, bug reports, and contributions to        */
/*        koethe@informatik.uni-hamburg.de                              */
/*                                                                      */
/*  THIS SOFTWARE IS PROVIDED AS IS AND WITHOUT ANY EXPRESS OR          */
/*  IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED      */
/*  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. */
/*                                                                      */
/************************************************************************/
 
 
#ifndef VIGRA_SEPARABLECONVOLUTION_HXX
#define VIGRA_SEPARABLECONVOLUTION_HXX

#include <cmath>
#include <vector>
#include "vigra/utilities.hxx"
#include "vigra/numerictraits.hxx"
#include "vigra/imageiteratoradapter.hxx"
#include "vigra/bordertreatment.hxx"
#include "vigra/gaussians.hxx"

namespace vigra {

/********************************************************/
/*                                                      */
/*                internalConvolveLineWrap              */
/*                                                      */
/********************************************************/

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void internalConvolveLineWrap(SrcIterator is, SrcIterator iend, SrcAccessor sa,
                              DestIterator id, DestAccessor da,
                              KernelIterator kernel, KernelAccessor ka,
                              int kleft, int kright)
{
  //    int w = iend - is;
    int w = std::distance( is, iend );
    
    typedef typename NumericTraits<typename 
                      SrcAccessor::value_type>::RealPromote SumType;
    
    SrcIterator ibegin = is;
    
    for(int x=0; x<w; ++x, ++is, ++id)
    {
        KernelIterator ik = kernel + kright;
        SumType sum = NumericTraits<SumType>::zero();    

        if(x < kright)
        {
            int x0 = x - kright;
            SrcIterator iss = iend + x0;
            
            for(; x0; ++x0, --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        
            iss = ibegin;
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        else if(w-x <= -kleft)
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = iend;
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
            
            int x0 = -kleft - w + x + 1;
            iss = ibegin;
            
            for(; x0; --x0, --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        else
        {
            SrcIterator iss = is - kright;
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        
        da.set(NumericTraits<typename 
                      DestAccessor::value_type>::fromRealPromote(sum), id);
    }
}

/********************************************************/
/*                                                      */
/*                internalConvolveLineClip              */
/*                                                      */
/********************************************************/

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor, 
          class Norm>
void internalConvolveLineClip(SrcIterator is, SrcIterator iend, SrcAccessor sa,
                              DestIterator id, DestAccessor da,
                              KernelIterator kernel, KernelAccessor ka,
                              int kleft, int kright, Norm norm)
{
  //    int w = iend - is;
    int w = std::distance( is, iend );
    
    typedef typename NumericTraits<typename 
                      SrcAccessor::value_type>::RealPromote SumType;
    
    SrcIterator ibegin = is;
    
    for(int x=0; x<w; ++x, ++is, ++id)
    {
        KernelIterator ik = kernel + kright;
        SumType sum = NumericTraits<SumType>::zero();    

        if(x < kright)
        {
            int x0 = x - kright;
            Norm clipped = NumericTraits<Norm>::zero(); 

            for(; x0; ++x0, --ik)
            {
                clipped += ka(ik);
            }
        
            SrcIterator iss = ibegin;
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
            
            sum = norm / (norm - clipped) * sum;
        }
        else if(w-x <= -kleft)
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = iend;
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
            
            Norm clipped = NumericTraits<Norm>::zero(); 

            int x0 = -kleft - w + x + 1;
            
            for(; x0; --x0, --ik)
            {
                clipped += ka(ik);
            }
            
            sum = norm / (norm - clipped) * sum;
        }
        else
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        
        da.set(NumericTraits<typename 
                      DestAccessor::value_type>::fromRealPromote(sum), id);
    }
}

/********************************************************/
/*                                                      */
/*             internalConvolveLineReflect              */
/*                                                      */
/********************************************************/

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void internalConvolveLineReflect(SrcIterator is, SrcIterator iend, SrcAccessor sa,
                              DestIterator id, DestAccessor da,
                              KernelIterator kernel, KernelAccessor ka,
                              int kleft, int kright)
{
  //    int w = iend - is;
    int w = std::distance( is, iend );
    
    typedef typename NumericTraits<typename 
                      SrcAccessor::value_type>::RealPromote SumType;
    
    SrcIterator ibegin = is;
    
    for(int x=0; x<w; ++x, ++is, ++id)
    {
        KernelIterator ik = kernel + kright;
        SumType sum = NumericTraits<SumType>::zero();    

        if(x < kright)
        {
            int x0 = x - kright;
            SrcIterator iss = ibegin - x0;
            
            for(; x0; ++x0, --ik, --iss)
            {
                sum += ka(ik) * sa(iss);
            }
        
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        else if(w-x <= -kleft)
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = iend;
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
            
            int x0 = -kleft - w + x + 1;
            iss = iend - 2;
            
            for(; x0; --x0, --ik, --iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        else
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        
        da.set(NumericTraits<typename 
                      DestAccessor::value_type>::fromRealPromote(sum), id);
    }
}

/********************************************************/
/*                                                      */
/*             internalConvolveLineRepeat               */
/*                                                      */
/********************************************************/

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void internalConvolveLineRepeat(SrcIterator is, SrcIterator iend, SrcAccessor sa,
                              DestIterator id, DestAccessor da,
                              KernelIterator kernel, KernelAccessor ka,
                              int kleft, int kright)
{
  //    int w = iend - is;
    int w = std::distance( is, iend );
    
    typedef typename NumericTraits<typename 
                      SrcAccessor::value_type>::RealPromote SumType;
    
    SrcIterator ibegin = is;
    
    for(int x=0; x<w; ++x, ++is, ++id)
    {
        KernelIterator ik = kernel + kright;
        SumType sum = NumericTraits<SumType>::zero();    

        if(x < kright)
        {
            int x0 = x - kright;
            SrcIterator iss = ibegin;
            
            for(; x0; ++x0, --ik)
            {
                sum += ka(ik) * sa(iss);
            }
        
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        else if(w-x <= -kleft)
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = iend;
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
            
            int x0 = -kleft - w + x + 1;
            iss = iend - 1;
            
            for(; x0; --x0, --ik)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        else
        {
            SrcIterator iss = is + (-kright);
            SrcIterator isend = is + (1 - kleft);
            for(; iss != isend ; --ik, ++iss)
            {
                sum += ka(ik) * sa(iss);
            }
        }
        
        da.set(NumericTraits<typename 
                      DestAccessor::value_type>::fromRealPromote(sum), id);
    }
}

/********************************************************/
/*                                                      */
/*              internalConvolveLineAvoid               */
/*                                                      */
/********************************************************/

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void internalConvolveLineAvoid(SrcIterator is, SrcIterator iend, SrcAccessor sa,
                              DestIterator id, DestAccessor da,
                              KernelIterator kernel, KernelAccessor ka,
                              int kleft, int kright)
{
  //    int w = iend - is;
    int w = std::distance( is, iend );
    
    typedef typename NumericTraits<typename 
                      SrcAccessor::value_type>::RealPromote SumType;
    
    is += kright;
    id += kright;
    
    for(int x=kright; x<w+kleft; ++x, ++is, ++id)
    {
        KernelIterator ik = kernel + kright;
        SumType sum = NumericTraits<SumType>::zero();    

        SrcIterator iss = is + (-kright);
        SrcIterator isend = is + (1 - kleft);
        for(; iss != isend ; --ik, ++iss)
        {
            sum += ka(ik) * sa(iss);
        }
        
        da.set(NumericTraits<typename 
                      DestAccessor::value_type>::fromRealPromote(sum), id);
    }
}

/********************************************************/
/*                                                      */
/*         Separable convolution functions              */
/*                                                      */
/********************************************************/

/** \addtogroup SeparableConvolution One-dimensional and separable convolution functions
    
    Perform 1D convolution and separable filtering in 2 dimensions.
    
    These generic convolution functions implement
    the standard convolution operation for a wide range of images and
    signals that fit into the required interface. They need a suitable 
    kernel to operate.
*/
//@{

/** \brief Performs a 1 dimensional convolution of the source signal using the given
    kernel. 
    
    The KernelIterator must point to the center iterator, and
    the kernel's size is given by its left (kleft <= 0) and right
    (kright >= 0) borders. The signal must always be larger than the kernel.
    At those positions where the kernel does not completely fit 
    into the signal's range, the specified \ref BorderTreatmentMode is 
    applied. 
    
    The signal's value_type (SrcAccessor::value_type) must be a
    linear space over the kernel's value_type (KernelAccessor::value_type),
    i.e. addition of source values, multiplication with kernel values,
    and NumericTraits must be defined. 
    The kernel's value_type must be an algebraic field,
    i.e. the arithmetic operations (+, -, *, /) and NumericTraits must
    be defined.
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor, 
                  class KernelIterator, class KernelAccessor>
        void convolveLine(SrcIterator is, SrcIterator isend, SrcAccessor sa,
                          DestIterator id, DestAccessor da,
                          KernelIterator ik, KernelAccessor ka,
                          int kleft, int kright, BorderTreatmentMode border)
    }
    \endcode
    
    
    use argument objects in conjunction with \ref ArgumentObjectFactories:
    \code
    namespace vigra {
        template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor, 
                  class KernelIterator, class KernelAccessor>
        void convolveLine(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                          pair<DestIterator, DestAccessor> dest,
                          tuple5<KernelIterator, KernelAccessor,
                                 int, int, BorderTreatmentMode> kernel)
    }
    \endcode
    
    <b> Usage:</b>
    
    <b>\#include</b> "<a href="separableconvolution_8hxx-source.html">vigra/separableconvolution.hxx</a>"
    
    
    \code
    std::vector<float> src, dest;    
    ...
    
    // define binomial filter of size 5
    static float kernel[] = 
           { 1.0/16.0, 4.0/16.0, 6.0/16.0, 4.0/16.0, 1.0/16.0};
           
    typedef vigra::StandardAccessor<float> FAccessor;
    typedef vigra::StandardAccessor<float> KernelAccessor;
    
    
    vigra::convolveLine(src.begin(), src.end(), FAccessor(), dest.begin(), FAccessor(),
             kernel+2, KernelAccessor(), -2, 2, BORDER_TREATMENT_REFLECT);
    //       ^^^^^^^^  this is the center of the kernel      
    
    \endcode

    <b> Required Interface:</b>
    
    \code
    RandomAccessIterator is, isend;
    RandomAccessIterator id;
    RandomAccessIterator ik;
    
    SrcAccessor src_accessor;
    DestAccessor dest_accessor;
    KernelAccessor kernel_accessor;
    
    NumericTraits<SrcAccessor::value_type>::RealPromote s = src_accessor(is);

    s = s + s;
    s = kernel_accessor(ik) * s;

    dest_accessor.set(
        NumericTraits<DestAccessor::value_type>::fromRealPromote(s), id);

    \endcode
    
    If border == BORDER_TREATMENT_CLIP:

    \code
    NumericTraits<KernelAccessor::value_type>::RealPromote k = kernel_accessor(ik);
    
    k = k + k;
    k = k - k;
    k = k * k;
    k = k / k;

    \endcode

    <b> Preconditions:</b>
    
    \code
    kleft <= 0
    kright >= 0
    iend - is >= kright + kleft + 1
    \endcode

    If border == BORDER_TREATMENT_CLIP: Sum of kernel elements must be
    != 0.

*/
template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void convolveLine(SrcIterator is, SrcIterator iend, SrcAccessor sa,
                  DestIterator id, DestAccessor da,
                  KernelIterator ik, KernelAccessor ka,
                  int kleft, int kright, BorderTreatmentMode border)
{
    typedef typename KernelAccessor::value_type KernelValue;
    
    vigra_precondition(kleft <= 0,
                 "convolveLine(): kleft must be <= 0.\n");
    vigra_precondition(kright >= 0,
                 "convolveLine(): kright must be >= 0.\n");
    
    //    int w = iend - is;
    int w = std::distance( is, iend );

    vigra_precondition(w >= kright - kleft + 1,
                 "convolveLine(): kernel longer than line\n");
    
    switch(border)
    {
      case BORDER_TREATMENT_WRAP:
      {
        internalConvolveLineWrap(is, iend, sa, id, da, ik, ka, kleft, kright);
        break;
      }
      case BORDER_TREATMENT_AVOID:
      {
        internalConvolveLineAvoid(is, iend, sa, id, da, ik, ka, kleft, kright);
        break;
      }
      case BORDER_TREATMENT_REFLECT:
      {
        internalConvolveLineReflect(is, iend, sa, id, da, ik, ka, kleft, kright);
        break;
      }
      case BORDER_TREATMENT_REPEAT:
      {
        internalConvolveLineRepeat(is, iend, sa, id, da, ik, ka, kleft, kright);
        break;
      }
      case BORDER_TREATMENT_CLIP:
      {
        // find norm of kernel
        typedef typename KernelAccessor::value_type KT;
        KT norm = NumericTraits<KT>::zero();
        KernelIterator iik = ik + kleft;
        for(int i=kleft; i<=kright; ++i, ++iik) norm += ka(iik);
        
        vigra_precondition(norm != NumericTraits<KT>::zero(),
                     "convolveLine(): Norm of kernel must be != 0"
                     " in mode BORDER_TREATMENT_CLIP.\n");
                     
        internalConvolveLineClip(is, iend, sa, id, da, ik, ka, kleft, kright, norm);
        break;
      }
      default:
      {
        vigra_precondition(0,
                     "convolveLine(): Unknown border treatment mode.\n");
      }        
    }
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
inline 
void convolveLine(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                  pair<DestIterator, DestAccessor> dest,
                  tuple5<KernelIterator, KernelAccessor,
                         int, int, BorderTreatmentMode> kernel)
{
    convolveLine(src.first, src.second, src.third,
                 dest.first, dest.second,
                 kernel.first, kernel.second, 
                 kernel.third, kernel.fourth, kernel.fifth);
}

/********************************************************/
/*                                                      */
/*                      separableConvolveX              */
/*                                                      */
/********************************************************/

/** \brief Performs a 1 dimensional convolution in x direction.

    It calls \link SeparableConvolution#convolveLine convolveLine\endlink() for every row of the
    image. See \link SeparableConvolution#convolveLine convolveLine\endlink() for more information about required interfaces
    and vigra_preconditions.
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor, 
                  class KernelIterator, class KernelAccessor>
        void separableConvolveX(SrcImageIterator supperleft, 
                                SrcImageIterator slowerright, SrcAccessor sa,
                                DestImageIterator dupperleft, DestAccessor da,
                                KernelIterator ik, KernelAccessor ka,
                                int kleft, int kright, BorderTreatmentMode border)
    }
    \endcode
    
    
    use argument objects in conjunction with \ref ArgumentObjectFactories:
    \code
    namespace vigra {
        template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor, 
                  class KernelIterator, class KernelAccessor>
        void separableConvolveX(triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                                pair<DestImageIterator, DestAccessor> dest,
                                tuple5<KernelIterator, KernelAccessor,
                                             int, int, BorderTreatmentMode> kernel)
    }
    \endcode
    
    <b> Usage:</b>
    
    <b>\#include</b> "<a href="separableconvolution_8hxx-source.html">vigra/separableconvolution.hxx</a>"
    
    
    \code
    vigra::FImage src(w,h), dest(w,h);    
    ...
    
    // define Gaussian kernel with std. deviation 3.0
    vigra::Kernel1D<double> kernel;
    kernel.initGaussian(3.0);
    
    vigra::separableConvolveX(srcImageRange(src), destImage(dest), kernel1d(kernel));
    
    \endcode

*/
template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void separableConvolveX(SrcIterator supperleft, 
                        SrcIterator slowerright, SrcAccessor sa,
                        DestIterator dupperleft, DestAccessor da,
                        KernelIterator ik, KernelAccessor ka,
                        int kleft, int kright, BorderTreatmentMode border)
{
    typedef typename KernelAccessor::value_type KernelValue;
    
    vigra_precondition(kleft <= 0,
                 "separableConvolveX(): kleft must be <= 0.\n");
    vigra_precondition(kright >= 0,
                 "separableConvolveX(): kright must be >= 0.\n");
    
    int w = slowerright.x - supperleft.x;
    int h = slowerright.y - supperleft.y;
    
    vigra_precondition(w >= kright - kleft + 1,
                 "separableConvolveX(): kernel longer than line\n");
    
    int y;
    
    for(y=0; y<h; ++y, ++supperleft.y, ++dupperleft.y)
    {
        typename SrcIterator::row_iterator rs = supperleft.rowIterator();
        typename DestIterator::row_iterator rd = dupperleft.rowIterator(); 
        
        convolveLine(rs, rs+w, sa, rd, da, 
                     ik, ka, kleft, kright, border);
    }
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
inline void 
separableConvolveX(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                  pair<DestIterator, DestAccessor> dest,
                  tuple5<KernelIterator, KernelAccessor,
                         int, int, BorderTreatmentMode> kernel)
{
    separableConvolveX(src.first, src.second, src.third,
                 dest.first, dest.second,
                 kernel.first, kernel.second, 
                 kernel.third, kernel.fourth, kernel.fifth);
}



/********************************************************/
/*                                                      */
/*                      separableConvolveY              */
/*                                                      */
/********************************************************/

/** \brief Performs a 1 dimensional convolution in y direction.

    It calls \link SeparableConvolution#convolveLine convolveLine\endlink() for every column of the
    image. See \link SeparableConvolution#convolveLine convolveLine\endlink() for more information about required interfaces
    and vigra_preconditions.
    
    <b> Declarations:</b>
    
    pass arguments explicitly:
    \code
    namespace vigra {
        template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor, 
                  class KernelIterator, class KernelAccessor>
        void separableConvolveY(SrcImageIterator supperleft, 
                                SrcImageIterator slowerright, SrcAccessor sa,
                                DestImageIterator dupperleft, DestAccessor da,
                                KernelIterator ik, KernelAccessor ka,
                                int kleft, int kright, BorderTreatmentMode border)
    }
    \endcode
    
    
    use argument objects in conjunction with \ref ArgumentObjectFactories:
    \code
    namespace vigra {
        template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor, 
                  class KernelIterator, class KernelAccessor>
        void separableConvolveY(triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                                pair<DestImageIterator, DestAccessor> dest,
                                tuple5<KernelIterator, KernelAccessor,
                                             int, int, BorderTreatmentMode> kernel)
    }
    \endcode
    
    <b> Usage:</b>
    
    <b>\#include</b> "<a href="separableconvolution_8hxx-source.html">vigra/separableconvolution.hxx</a>"
    
    
    \code
    vigra::FImage src(w,h), dest(w,h);    
    ...
    
    // define Gaussian kernel with std. deviation 3.0
    vigra::Kernel1D kernel;
    kernel.initGaussian(3.0);
    
    vigra::separableConvolveY(srcImageRange(src), destImage(dest), kernel1d(kernel));
    
    \endcode

*/
template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
void separableConvolveY(SrcIterator supperleft, 
                        SrcIterator slowerright, SrcAccessor sa,
                        DestIterator dupperleft, DestAccessor da,
                        KernelIterator ik, KernelAccessor ka,
                        int kleft, int kright, BorderTreatmentMode border)
{
    typedef typename KernelAccessor::value_type KernelValue;
    
    vigra_precondition(kleft <= 0,
                 "separableConvolveY(): kleft must be <= 0.\n");
    vigra_precondition(kright >= 0,
                 "separableConvolveY(): kright must be >= 0.\n");
    
    int w = slowerright.x - supperleft.x;
    int h = slowerright.y - supperleft.y;
    
    vigra_precondition(h >= kright - kleft + 1,
                 "separableConvolveY(): kernel longer than line\n");
    
    int x;
    
    for(x=0; x<w; ++x, ++supperleft.x, ++dupperleft.x)
    {
        typename SrcIterator::column_iterator cs = supperleft.columnIterator();
        typename DestIterator::column_iterator cd = dupperleft.columnIterator(); 
        
        convolveLine(cs, cs+h, sa, cd, da, 
                     ik, ka, kleft, kright, border);
    }
}

template <class SrcIterator, class SrcAccessor,
          class DestIterator, class DestAccessor, 
          class KernelIterator, class KernelAccessor>
inline void 
separableConvolveY(triple<SrcIterator, SrcIterator, SrcAccessor> src,
                  pair<DestIterator, DestAccessor> dest,
                  tuple5<KernelIterator, KernelAccessor,
                         int, int, BorderTreatmentMode> kernel)
{
    separableConvolveY(src.first, src.second, src.third,
                 dest.first, dest.second,
                 kernel.first, kernel.second, 
                 kernel.third, kernel.fourth, kernel.fifth);
}

//@}

/********************************************************/
/*                                                      */
/*                      Kernel1D                        */
/*                                                      */
/********************************************************/

/** \brief Generic 1 dimensional convolution kernel.

    This kernel may be used for convolution of 1 dimensional signals or for
    separable convolution of multidimensional signals. 
    
    Convlution functions access the kernel via a 1 dimensional random access
    iterator which they get by calling \ref center(). This iterator
    points to the center of the kernel. The kernel's size is given by its left() (<=0) 
    and right() (>= 0) methods. The desired border treatment mode is
    returned by borderTreatment().
    
    The different init functions create a kernel with the specified
    properties. The kernel's value_type must be a linear space, i.e. it 
    must define multiplication with doubles and NumericTraits.
    
    
    The kernel defines a factory function kernel1d() to create an argument object
    (see \ref KernelArgumentObjectFactories).

    <b> Usage:</b>
    
    <b>\#include</b> "<a href="stdconvolution_8hxx-source.html">vigra/stdconvolution.hxx</a>"
    
    \code
    vigra::FImage src(w,h), dest(w,h);    
    ...
    
    // define Gaussian kernel with std. deviation 3.0
    vigra::Kernel1D kernel;
    kernel.initGaussian(3.0);
    
    vigra::separableConvolveX(srcImageRange(src), destImage(dest), kernel1d(kernel));
    \endcode

    <b> Required Interface:</b>
    
    \code
    value_type v = vigra::NumericTraits<value_type>::one(); // if norm is not
                                                            // given explicitly
    double d;
    
    v = d * v; 
    \endcode
*/

template <class ARITHTYPE>
class Kernel1D
{
  public:
        /** the kernel's value type
        */
    typedef typename std::vector<ARITHTYPE>::value_type value_type;
    
        /** the kernel's reference type
        */
    typedef typename std::vector<ARITHTYPE>::reference reference;
    
        /** the kernel's const reference type
        */
    typedef typename std::vector<ARITHTYPE>::const_reference const_reference;
    
        /** deprecated -- use Kernel1D::iterator
        */
    typedef typename std::vector<ARITHTYPE>::iterator Iterator;
    
        /** 1D random access iterator over the kernel's values
        */
    typedef typename std::vector<ARITHTYPE>::iterator iterator;
    
        /** const 1D random access iterator over the kernel's values
        */
    typedef typename std::vector<ARITHTYPE>::const_iterator const_iterator;
    
        /** the kernel's accessor
        */
    typedef StandardAccessor<ARITHTYPE> Accessor;
    
        /** the kernel's const accessor
        */
    typedef StandardConstAccessor<ARITHTYPE> ConstAccessor;
    
    struct InitProxy
    {
        InitProxy(Iterator i, int count, value_type & norm)
        : iter_(i), base_(i),
          count_(count), sum_(count),
          norm_(norm)
        {}
        
        ~InitProxy()
        {
            vigra_precondition(count_ == 1 || count_ == sum_,
                  "Kernel1D::initExplicitly(): "
                  "Too few init values.");
        }
        
        InitProxy & operator,(value_type const & v)
        {
            if(sum_ == count_) norm_ = *iter_;
            
            norm_ += v;
            
            --count_;
            vigra_precondition(count_ > 0,
                  "Kernel1D::initExplicitly(): "
                  "Too many init values.");
                  
            ++iter_;
            *iter_ = v;
            
            return *this;
        }
        
        Iterator iter_, base_;
        int count_, sum_;
        value_type & norm_;
    };
    
    static value_type one() { return NumericTraits<value_type>::one(); }
    
        /** Default constructor.
            Creates a kernel of size 1 which would copy the signal
            unchanged.
        */
    Kernel1D()
    : kernel_(),
      left_(0),
      right_(0),
      border_treatment_(BORDER_TREATMENT_CLIP),
      norm_(one())
    {
        kernel_.push_back(norm_);
    }
    
        /** Copy constructor.
        */
    Kernel1D(Kernel1D const & k)
    : kernel_(k.kernel_),
      left_(k.left_),
      right_(k.right_),
      border_treatment_(k.border_treatment_),
      norm_(k.norm_)
    {}
    
        /** Copy assignment.
        */
    Kernel1D & operator=(Kernel1D const & k)
    {
        if(this != &k)
        {
            left_ = k.left_;
            right_ = k.right_;
            border_treatment_ = k.border_treatment_;
            norm_ = k.norm_;
            kernel_ = k.kernel_;
        }
        return *this;
    }
    
        /** Initialization. 
            This initializes the kernel with the given constant. The norm becomes
            v*size().
            
            Instead of a single value an initializer list of length size() 
            can be used like this:
            
            \code
            vigra::Kernel1D<float> roberts_gradient_x;
            
            roberts_gradient_x.initExplicitly(0, 1) = 1.0, -1.0;
            \endcode
            
            In this case, the norm will be set to the sum of the init values. 
            An initializer list of wrong length will result in a run-time error.
        */
    InitProxy operator=(value_type const & v)
    {
        int size = right_ - left_ + 1;
        for(unsigned int i=0; i<kernel_.size(); ++i) kernel_[i] = v;
        norm_ = (double)size*v;
        
        return InitProxy(kernel_.begin(), size, norm_);
    }
    
        /** Destructor.
        */
    ~Kernel1D() 
    {}
    
        /** 
            Init as a sampled Gaussian function. The radius of the kernel is 
            always 3*std_dev. '<tt>norm</tt>' denotes the sum of all bins of the kernel
            (i.e. the kernel is corrected for the normalization error introduced
             by windowing the Gaussian to a finite interval). However,
            if <tt>norm</tt> is 0.0, the kernel is normalized to 1 by the analytic 
            expression for the Gaussian, and <b>no</b> correction for the windowing 
            error is performed.
            
            Precondition:  
            \code
            std_dev >= 0.0
            \endcode
            
            Postconditions: 
            \code
            1. left()  == -(int)(3.0*std_dev + 0.5)
            2. right() ==  (int)(3.0*std_dev + 0.5)
            3. borderTreatment() == BORDER_TREATMENT_CLIP
            4. norm() == norm
            \endcode
        */
    void initGaussian(double std_dev, value_type norm);
    
        /** Init as a Gaussian function with norm 1. 
         */
    void initGaussian(double std_dev) 
    {
        initGaussian(std_dev, one());
    }
    
    
        /** 
            Init as Lindeberg's discrete analog of the Gaussian function. The radius of the kernel is 
            always 3*std_dev. 'norm' denotes the sum of all bins of the kernel.
            
            Precondition:  
            \code
            std_dev >= 0.0
            \endcode
            
            Postconditions: 
            \code
            1. left()  == -(int)(3.0*std_dev + 0.5)
            2. right() ==  (int)(3.0*std_dev + 0.5)
            3. borderTreatment() == BORDER_TREATMENT_REFLECT
            4. norm() == norm
            \endcode
        */
    void initDiscreteGaussian(double std_dev, value_type norm);
    
        /** Init as a LOineberg's discrete analog of the Gaussian function 
            with norm 1. 
         */
    void initDiscreteGaussian(double std_dev) 
    {
        initDiscreteGaussian(std_dev, one());
    }
    
        /** 
            Init as a Gaussian derivative of order '<tt>order</tt>'. 
            The radius of the kernel is always <tt>3*std_dev + 0.5*order</tt>.
            '<tt>norm</tt>' denotes the norm of the kernel so that the
            following condition is fulfilled:
              
            \f[ \sum_{i=left()}^{right()} 
                         \frac{(-i)^{order}kernel[i]}{order!} = norm
            \f]
            
            Thus, the kernel will be corrected for the error introduced
            by windowing the Gaussian to a finite interval. However, 
            if <tt>norm</tt> is 0.0, the kernel is normalized to 1 by the analytic 
            expression for the Gaussian derivative, and <b>no</b> correction for the 
            windowing error is performed.
            
            Preconditions:  
            \code
            1. std_dev >= 0.0
            2. order   >= 1
            \endcode
            
            Postconditions: 
            \code
            1. left()  == -(int)(3.0*std_dev + 0.5*order + 0.5)
            2. right() ==  (int)(3.0*std_dev + 0.5*order + 0.5)
            3. borderTreatment() == BORDER_TREATMENT_REPEAT
            4. norm() == norm
            \endcode
        */
    void initGaussianDerivative(double std_dev, int order, value_type norm);
    
        /** Init as a Gaussian derivative with norm 1. 
         */
    void initGaussianDerivative(double std_dev, int order) 
    {
        initGaussianDerivative(std_dev, order, one());
    }
    
        /** 
            Init as a Binomial filter. 'norm' denotes the sum of all bins 
            of the kernel.
            
            Precondition:  
            \code
            radius   >= 0
            \endcode
            
            Postconditions: 
            \code
            1. left()  == -radius
            2. right() ==  radius
            3. borderTreatment() == BORDER_TREATMENT_REFLECT
            4. norm() == norm
            \endcode
        */
    void initBinomial(int radius, value_type norm);
    
        /** Init as a Binomial filter with norm 1. 
         */
    void initBinomial(int radius) 
    {
        initBinomial(radius, one());
    }
    
        /** 
            Init as an Averaging filter. 'norm' denotes the sum of all bins 
            of the kernel. The window size is (2*radius+1) * (2*radius+1)
            
            Precondition:  
            \code
            radius   >= 0
            \endcode
            
            Postconditions: 
            \code
            1. left()  == -radius
            2. right() ==  radius
            3. borderTreatment() == BORDER_TREATMENT_CLIP
            4. norm() == norm
            \endcode
        */
    void initAveraging(int radius, value_type norm);
    
        /** Init as a Averaging filter with norm 1. 
         */
    void initAveraging(int radius) 
    {
        initAveraging(radius, one());
    }
    
        /** 
            Init as a symmetric gradient filter of the form
           <TT>[ 0.5 * norm, 0.0 * norm, -0.5 * norm]</TT>
            
            Postconditions: 
            \code
            1. left()  == -1
            2. right() ==  1
            3. borderTreatment() == BORDER_TREATMENT_REPEAT
            4. norm() == norm
            \endcode
        */
    void 
    initSymmetricGradient(value_type norm );
    
        /** Init as a symmetric gradient filter with norm 1. 
         */
    void initSymmetricGradient() 
    {
        initSymmetricGradient(one());
    }
    
        /** Init the kernel by an explicit initializer list.
            The left and right boundaries of the kernel must be passed.
            A comma-separated initializer list is given after the assignment 
            operator. This function is used like this:
                
            \code
            // define horizontal Roberts filter
            vigra::Kernel1D<float> roberts_gradient_x;
            
            roberts_gradient_x.initExplicitly(0, 1) = 1.0, -1.0;
            \endcode
            
            The norm is set to the sum of the initialzer values. If the wrong number of 
            values is given, a run-time error results. It is, however, possible to give 
            just one initializer. This creates an averaging filter with the given constant:
            
            \code
            vigra::Kernel1D<float> average5x1;
            
            average5x1.initExplicitly(-2, 2) = 1.0/5.0;
            \endcode
            
            Here, the norm is set to value*size().
                
            <b> Preconditions:</b>
            
            \code
            
            1. left <= 0
            2. right >= 0
            3. the number of values in the initializer list 
               is 1 or equals the size of the kernel.
            \endcode
        */
    Kernel1D & initExplicitly(int left, int right)
    {
        vigra_precondition(left <= 0,
                     "Kernel1D::initExplicitly(): left border must be <= 0.");
        vigra_precondition(right >= 0,
                     "Kernel1D::initExplicitly(): right border must be <= 0.");
    
        right_ = right;
        left_ = left;
        
        kernel_.resize(right - left + 1);
        
        return *this;
    }
    
        /** Get iterator to center of kernel 
            
            Postconditions: 
            \code
            
            center()[left()] ... center()[right()] are valid kernel positions 
            \endcode
        */
    iterator center() 
    {
        return kernel_.begin() - left();
    }
    
    const_iterator center() const
    {
        return kernel_.begin() - left();
    }
    
        /** Access kernel value at specified location. 
            
            Preconditions: 
            \code
            
            left() <= location <= right() 
            \endcode
        */
    reference operator[](int location) 
    {
        return kernel_[location - left()];
    }
    
    const_reference operator[](int location) const
    {
        return kernel_[location - left()];
    }
    
        /** left border of kernel (inclusive), always <= 0
        */
    int left() const { return left_; }
    
        /** right border of kernel (inclusive), always >= 0
        */
    int right() const { return right_; }
    
        /** size of kernel (right() - left() + 1)
        */
    int size() const { return right_ - left_ + 1; }
    
        /** current border treatment mode
        */
    BorderTreatmentMode borderTreatment() const 
    { return border_treatment_; }
    
        /** Set border treatment mode. 
        */
    void setBorderTreatment( BorderTreatmentMode new_mode)
    { border_treatment_ = new_mode; }
    
        /** norm of kernel
        */
    value_type norm() const { return norm_; }
    
        /** set a new norm and normalize kernel, use the normalization formula
            for the given </tt>derivativeOrder</tt>.
        */
    void
    normalize(value_type norm, unsigned int derivativeOrder = 0, double offset = 0.0);
    
        /** normalize kernel to norm 1.
        */
    void
    normalize() 
    { 
        normalize(one());
    }
    
        /** get a const accessor
        */
    ConstAccessor accessor() const { return ConstAccessor(); }   
    
        /** get an accessor
        */
    Accessor accessor() { return Accessor(); }
    
  private:
    std::vector<value_type> kernel_;
    int left_, right_;
    BorderTreatmentMode border_treatment_;
    value_type norm_;
};

template <class ARITHTYPE>
void Kernel1D<ARITHTYPE>::normalize(value_type norm, 
                          unsigned int derivativeOrder,
                          double offset) 
{
    typedef typename NumericTraits<value_type>::RealPromote TmpType;    
    
    // find kernel sum
    Iterator k = kernel_.begin();
    TmpType sum = NumericTraits<TmpType>::zero();
    
    if(derivativeOrder == 0)
    {
        for(; k < kernel_.end(); ++k)  
        {
            sum += *k;
        }
    }
    else
    {
        unsigned int faculty = 1;
        for(unsigned int i = 2; i <= derivativeOrder; ++i)
            faculty *= i;
        for(double x = left() + offset; k < kernel_.end(); ++x, ++k)  
        {
            sum += *k * VIGRA_CSTD::pow(-x, int(derivativeOrder)) / faculty;
        }
    }
    
    vigra_precondition(sum != NumericTraits<value_type>::zero(),
                    "Kernel1D<ARITHTYPE>::normalize(): "
                    "Cannot normalize a kernel with sum = 0");
    // normalize
    sum = norm / sum;
    k = kernel_.begin();
    for(; k != kernel_.end(); ++k)  
    {
        *k = *k * sum;
    }        

    norm_ = norm;
}
    
/***********************************************************************/

template <class ARITHTYPE>
void Kernel1D<ARITHTYPE>::initGaussian(double std_dev, 
                                       value_type norm)
{
    vigra_precondition(std_dev >= 0.0,
              "Kernel1D::initGaussian(): Standard deviation must be >= 0.");
    
    if(std_dev > 0.0)
    {              
        Gaussian<ARITHTYPE> gauss(std_dev);
        
        // first calculate required kernel sizes
        int radius = (int)(3.0 * std_dev + 0.5);
        if(radius == 0)
            radius = 1;

        // allocate the kernel
        kernel_.erase(kernel_.begin(), kernel_.end());
        kernel_.reserve(radius*2+1);

        for(ARITHTYPE x = -radius; x <= radius; ++x)
        {
            kernel_.push_back(gauss(x));
        }
        left_ = -radius;
        right_ = radius;
    }
    else
    {
        kernel_.erase(kernel_.begin(), kernel_.end());
        kernel_.push_back(1.0);
        left_ = 0;
        right_ = 0;
    }
    
    if(norm != 0.0)
        normalize(norm);
    else
        norm_ = 1.0;

    // best border treatment for Gaussians is BORDER_TREATMENT_CLIP
    border_treatment_ = BORDER_TREATMENT_CLIP;  
}

/***********************************************************************/

template <class ARITHTYPE>
void Kernel1D<ARITHTYPE>::initDiscreteGaussian(double std_dev, 
                                       value_type norm)
{
    vigra_precondition(std_dev >= 0.0,
              "Kernel1D::initDiscreteGaussian(): Standard deviation must be >= 0.");
              
    if(std_dev > 0.0)
    {
        // first calculate required kernel sizes
        int radius = (int)(3.0*std_dev + 0.5);
        if(radius == 0)
            radius = 1;
            
        double f = 2.0 / std_dev / std_dev;

        // allocate the working array
        int maxIndex = (int)(2.0 * (radius + 5.0 * VIGRA_CSTD::sqrt((double)radius)) + 0.5);
        std::vector<double> warray(maxIndex+1);
        warray[maxIndex] = 0.0;
        warray[maxIndex-1] = 1.0;
        
        for(int i = maxIndex-2; i >= radius; --i)
        {
            warray[i] = warray[i+2] + f * (i+1) * warray[i+1];
            if(warray[i] > 1.0e40)
            {
                warray[i+1] /= warray[i];
                warray[i] = 1.0;
            }
        }
        
        // the following rescaling ensures that the numbers stay in a sensible range 
        // during the rest of the iteration, so no other rescaling is needed
        double er = VIGRA_CSTD::exp(-radius*radius / (2.0*std_dev*std_dev));
        warray[radius+1] = er * warray[radius+1] / warray[radius];
        warray[radius] = er;
        
        for(int i = radius-1; i >= 0; --i)
        {
            warray[i] = warray[i+2] + f * (i+1) * warray[i+1];
            er += warray[i];
        }
        
        double scale = norm / (2*er - warray[0]);
    
        initExplicitly(-radius, radius);
        iterator c = center();

        for(int i=0; i<=radius; ++i)
        {
            c[i] = c[-i] = warray[i] * scale;
        }
    }
    else
    {
        kernel_.erase(kernel_.begin(), kernel_.end());
        kernel_.push_back(norm);
        left_ = 0;
        right_ = 0;
    }
    
    norm_ = norm;

    // best border treatment for Gaussians is BORDER_TREATMENT_REFLECT
    border_treatment_ = BORDER_TREATMENT_REFLECT;  
}

/***********************************************************************/

template <class ARITHTYPE>
void 
Kernel1D<ARITHTYPE>::initGaussianDerivative(double std_dev, 
                    int order,
                    value_type norm)
{
    vigra_precondition(order >= 0,
              "Kernel1D::initGaussianDerivative(): Order must be >= 0.");
              
    if(order == 0)
    {
        initGaussian(std_dev, norm);
        return;
    }
              
    vigra_precondition(std_dev > 0.0,
              "Kernel1D::initGaussianDerivative(): "
              "Standard deviation must be > 0.");
              
    Gaussian<ARITHTYPE> gauss(std_dev, order);
    
    // first calculate required kernel sizes
    int radius = (int)(3.0 * std_dev + 0.5 * order + 0.5);
    if(radius == 0)
        radius = 1;
   
    // allocate the kernels
    kernel_.clear();
    kernel_.reserve(radius*2+1);

    // fill the kernel and calculate the DC component 
    // introduced by truncation of the Geussian
    ARITHTYPE dc = 0.0;
    for(ARITHTYPE x = -radius; x <= radius; ++x)
    {
        kernel_.push_back(gauss(x));
        dc += kernel_[kernel_.size()-1];
    }
    dc /= (2.0*radius + 1.0);
                
    // remove DC, but only if kernel correction is permitted by a non-zero
    // value for norm
    if(norm != 0.0)
    {
        for(unsigned int i=0; i < kernel_.size(); ++i)
        {
            kernel_[i] -= dc;
        }
    }
    
    left_ = -radius;
    right_ = radius;

    if(norm != 0.0)
        normalize(norm, order);
    else
        norm_ = 1.0;

    // best border treatment for Gaussian derivatives is 
    // BORDER_TREATMENT_REPEAT
    border_treatment_ = BORDER_TREATMENT_REPEAT;  
}

/***********************************************************************/

template <class ARITHTYPE>
void 
Kernel1D<ARITHTYPE>::initBinomial(int radius, 
                                  value_type norm)
{
    vigra_precondition(radius > 0,
              "Kernel1D::initBinomial(): Radius must be > 0.");
              
    // allocate the kernel
    std::vector<double> kernel(radius*2+1);
    
    int i,j;
    for(i=0; i<radius*2+1; ++i) kernel[i] = 0;
    
    // fill kernel
    std::vector<double>::iterator x = kernel.begin() + radius;
    x[radius] = 1.0;
    
    for(j=radius-1; j>=-radius; --j)
    {
        for(i=j; i<radius; ++i)
        {
            x[i] = (x[i] + x[i+1]) / 2.0;
        }
        x[radius] /= 2.0;
    }
    
    // normalize
    kernel_.erase(kernel_.begin(), kernel_.end());
    kernel_.reserve(radius*2+1);
    
    for(i=0; i<=radius*2+1; ++i)
    {
        kernel_.push_back(kernel[i] * norm);
    }
    
    left_ = -radius;
    right_ = radius;
    norm_ = norm;

    // best border treatment for Binomial is BORDER_TREATMENT_REFLECT
    border_treatment_ = BORDER_TREATMENT_REFLECT;  
}
               
/***********************************************************************/

template <class ARITHTYPE>
void Kernel1D<ARITHTYPE>::initAveraging(int radius, 
                                        value_type norm)
{
    vigra_precondition(radius > 0,
              "Kernel1D::initAveraging(): Radius must be > 0.");
              
    // calculate scaling
    double scale = 1.0 / (radius * 2 + 1);
    
    // normalize
    kernel_.erase(kernel_.begin(), kernel_.end());
    kernel_.reserve(radius*2+1);
    
    for(int i=0; i<=radius*2+1; ++i)
    {
        kernel_.push_back(scale * norm);
    }
    
    left_ = -radius;
    right_ = radius;
    norm_ = norm;

    // best border treatment for Averaging is BORDER_TREATMENT_CLIP
    border_treatment_ = BORDER_TREATMENT_CLIP;  
}

/***********************************************************************/

template <class ARITHTYPE>
void 
Kernel1D<ARITHTYPE>::initSymmetricGradient(value_type norm)
{
    kernel_.erase(kernel_.begin(), kernel_.end());
    kernel_.reserve(3);
    
    kernel_.push_back(0.5 * norm);
    kernel_.push_back(0.0 * norm);
    kernel_.push_back(-0.5 * norm);
    
    left_ = -1;
    right_ = 1;
    norm_ = norm;

    // best border treatment for SymmetricGradient is 
    // BORDER_TREATMENT_REPEAT
    border_treatment_ = BORDER_TREATMENT_REPEAT;  
}

/**************************************************************/
/*                                                            */
/*         Argument object factories for Kernel1D             */
/*                                                            */
/*     (documentation: see vigra/convolution.hxx)             */
/*                                                            */
/**************************************************************/

template <class KernelIterator, class KernelAccessor>
inline
tuple5<KernelIterator, KernelAccessor, int, int, BorderTreatmentMode>
kernel1d(KernelIterator ik, KernelAccessor ka,
       int kleft, int kright, BorderTreatmentMode border)
{
    return 
      tuple5<KernelIterator, KernelAccessor, int, int, BorderTreatmentMode>(
                                                ik, ka, kleft, kright, border);
}

template <class T>
inline
tuple5<typename Kernel1D<T>::const_iterator, typename Kernel1D<T>::ConstAccessor, 
       int, int, BorderTreatmentMode>
kernel1d(Kernel1D<T> const & k)

{
    return 
        tuple5<typename Kernel1D<T>::const_iterator, typename Kernel1D<T>::ConstAccessor, 
               int, int, BorderTreatmentMode>(
                                     k.center(), 
                                     k.accessor(), 
                                     k.left(), k.right(), 
                                     k.borderTreatment());
}

template <class T>
inline
tuple5<typename Kernel1D<T>::const_iterator, typename Kernel1D<T>::ConstAccessor, 
       int, int, BorderTreatmentMode>
kernel1d(Kernel1D<T> const & k, BorderTreatmentMode border)

{
    return 
        tuple5<typename Kernel1D<T>::const_iterator, typename Kernel1D<T>::ConstAccessor, 
               int, int, BorderTreatmentMode>(
                                     k.center(), 
                                     k.accessor(), 
                                     k.left(), k.right(), 
                                     border);
}


} // namespace vigra

#endif // VIGRA_SEPARABLECONVOLUTION_HXX
