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| Recursive convolution functions |

## Functions
template<... >
void | recursiveFilterLine (...)
| Performs a 1-dimensional recursive convolution of the source signal.
template<... >
void | recursiveFilterX (...)
| Performs 1 dimensional recursive filtering (1st and 2nd order) in x direction.
template<... >
void | recursiveFilterY (...)
| Performs 1 dimensional recursive filtering (1st and 2nd order) in y direction.
template<... >
void | recursiveFirstDerivativeLine (...)
| Performs a 1 dimensional recursive convolution of the source signal.
template<... >
void | recursiveFirstDerivativeX (...)
| Recursively calculates the 1 dimensional first derivative in x direction.
template<... >
void | recursiveFirstDerivativeY (...)
| Recursively calculates the 1 dimensional first derivative in y direction.
template<... >
void | recursiveGaussianFilterLine (...)
| Compute a 1-dimensional recursive approximation of Gaussian smoothing.
template<... >
void | recursiveGaussianFilterX (...)
| Compute 1 dimensional recursive approximation of Gaussian smoothing in y direction.
template<... >
void | recursiveGaussianFilterY (...)
| Compute 1 dimensional recursive approximation of Gaussian smoothing in y direction.
template<... >
void | recursiveSecondDerivativeLine (...)
| Performs a 1 dimensional recursive convolution of the source signal.
template<... >
void | recursiveSecondDerivativeX (...)
| Recursively calculates the 1 dimensional second derivative in x direction.
template<... >
void | recursiveSecondDerivativeY (...)
| Recursively calculates the 1 dimensional second derivative in y direction.
template<... >
void | recursiveSmoothLine (...)
| Convolves the image with a 1-dimensional exponential filter.
template<... >
void | recursiveSmoothX (...)
| Performs 1 dimensional recursive smoothing in x direction.
template<... >
void | recursiveSmoothY (...)
| Performs 1 dimensional recursive smoothing in y direction.

## Detailed Description

First order recursive filters and their specialization for the exponential filter and its derivatives (1D and separable 2D). These filters are very fast, and the speed does not depend on the filter size.

## Function Documentation

void vigra::recursiveFilterLine | ( |  | ... | ) |

Performs a 1-dimensional recursive convolution of the source signal.

The function performs a causal and an anti-causal first or second order recursive filtering with the given filter parameter `b1` and border treatment `border` (first order filter, `b2 = 0`) or parameters `b1, b2` and `BORDER_TREATMENT_REFLECT` (second order filter). Thus, the result is always a filtering with linear phase.

The signal's value_type (SrcAccessor::value_type) must be a linear space over `double`, i.e. addition of source values, multiplication with `double`, and `NumericTraits` must be defined.

Declaration:

First order recursive filter:

        namespace vigra {
            template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
            void recursiveFilterLine(SrcIterator is, SrcIterator isend, SrcAccessor as,
                         DestIterator id, DestAccessor ad,
                         double b1, BorderTreatmentMode border)
        }

Second order recursive filter:

        namespace vigra {
            template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
            void recursiveFilterLine(SrcIterator is, SrcIterator isend, SrcAccessor as,
                         DestIterator id, DestAccessor ad,
                         double b1, double b2)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vector<float> src, dest;
        ...

        vigra::DefaultAccessor<vector<float>::iterator, float> FAccessor;

        vigra::recursiveFilterLine(src.begin(), src.end(), FAccessor(),
                                   dest.begin(), FAccessor(),
                                   0.5, BORDER_TREATMENT_REFLECT);

Required Interface:

        RandomAccessIterator is, isend;
        RandomAccessIterator id;

        SrcAccessor src_accessor;
        DestAccessor dest_accessor;

        NumericTraits<SrcAccessor::value_type>::RealPromote s = src_accessor(is);
        double d;

        s = s + s;
        s = d * s;

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

Preconditions:

        -1 < b  < 1

void vigra::recursiveGaussianFilterLine | ( |  | ... | ) |

Compute a 1-dimensional recursive approximation of Gaussian smoothing.

The function applies a causal and an anti-causal third order recursive filter which optimally approximates the Gaussian filter, as proposed in

I. Young, L. van Vliet: Recursive implementation of the Gaussian filter
Signal Processing 44:139-151, 1995

The formulas for transforming the given scale parameter `sigma` into the actual filter coefficients are taken from Luigi Rosa's Matlab implementation.

The signal's value_type (SrcAccessor::value_type) must be a linear space over `double`, i.e. addition of source values, multiplication with `double`, and `NumericTraits` must be defined.

Declaration:

        namespace vigra {
            template <class SrcIterator, class SrcAccessor,
                      class DestIterator, class DestAccessor>
            void
            recursiveGaussianFilterLine(SrcIterator is, SrcIterator isend, SrcAccessor as,
                                        DestIterator id, DestAccessor ad,
                                        double sigma);
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vector<float> src, dest;
        ...

        vigra::DefaultAccessor<vector<float>::iterator, float> FAccessor;
        double sigma = 2.5;

        vigra::recursiveGaussianFilterLine(src.begin(), src.end(), FAccessor(),
                                           dest.begin(), FAccessor(),
                                           sigma);

Required Interface:

        RandomAccessIterator is, isend;
        RandomAccessIterator id;

        SrcAccessor src_accessor;
        DestAccessor dest_accessor;

        NumericTraits<SrcAccessor::value_type>::RealPromote s = src_accessor(is);
        double d;

        s = s + s;
        s = d * s;

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

Preconditions:

        0 <= sigma (absolute values are used for negative sigma)

void vigra::recursiveSmoothLine | ( |  | ... | ) |

Convolves the image with a 1-dimensional exponential filter.

This function calls recursiveFilterLine() with `b = exp(-1.0/scale)` and `border = BORDER_TREATMENT_REPEAT`. See recursiveFilterLine() for more documentation.

Declaration:

        namespace vigra {
            template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
            void recursiveSmoothLine(SrcIterator is, SrcIterator isend, SrcAccessor as,
                         DestIterator id, DestAccessor ad, double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vector<float> src, dest;
        ...

        vigra::DefaultAccessor<vector<float>::iterator, float> FAccessor;

        vigra::recursiveSmoothLine(src.begin(), src.end(), FAccessor(),
                            dest.begin(), FAccessor(), 3.0);

Required Interface:

        RandomAccessIterator is, isend;
        RandomAccessIterator id;

        SrcAccessor src_accessor;
        DestAccessor dest_accessor;

        NumericTraits<SrcAccessor::value_type>::RealPromote s = src_accessor(is);
        double d;

        s = s + s;
        s = d * s;

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

Preconditions:

        scale > 0

void vigra::recursiveFirstDerivativeLine | ( |  | ... | ) |

Performs a 1 dimensional recursive convolution of the source signal.

It uses the first derivative an exponential `d/dx exp(-abs(x)/scale)` as a kernel. The signal's value_type (SrcAccessor::value_type) must be a linear space over `double`, i.e. addition and subtraction of source values, multiplication with `double`, and `NumericTraits` must be defined. Border treatment is always `BORDER_TREATMENT_REPEAT`.

Declaration:

        namespace vigra {
            template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
            void recursiveFirstDerivativeLine(SrcIterator is, SrcIterator isend, SrcAccessor as,
                         DestIterator id, DestAccessor ad, double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vector<float> src, dest;
        ...

        vigra::DefaultAccessor<vector<float>::iterator, float> FAccessor;

        vigra::recursiveFirstDerivativeLine(src.begin(), src.end(), FAccessor(),
                            dest.begin(), FAccessor(), 3.0);

Required Interface:

        RandomAccessIterator is, isend;
        RandomAccessIterator id;

        SrcAccessor src_accessor;
        DestAccessor dest_accessor;

        NumericTraits<SrcAccessor::value_type>::RealPromote s = src_accessor(is);
        double d;

        s = s + s;
        s = -s;
        s = d * s;

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

Preconditions:

        scale > 0

void vigra::recursiveSecondDerivativeLine | ( |  | ... | ) |

Performs a 1 dimensional recursive convolution of the source signal.

It uses the second derivative an exponential `d2/dx2 exp(-abs(x)/scale)` as a kernel. The signal's value_type (SrcAccessor::value_type) must be a linear space over `double`, i.e. addition and subtraction of source values, multiplication with `double`, and `NumericTraits` must be defined. Border treatment is always `BORDER_TREATMENT_REPEAT`.

Declaration:

        namespace vigra {
            template <class SrcIterator, class SrcAccessor,
                  class DestIterator, class DestAccessor>
            void recursiveSecondDerivativeLine(SrcIterator is, SrcIterator isend, SrcAccessor as,
                         DestIterator id, DestAccessor ad, double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vector<float> src, dest;
        ...

        vigra::DefaultAccessor<vector<float>::iterator, float> FAccessor;

        vigra::recursiveSecondDerivativeLine(src.begin(), src.end(), FAccessor(),
                            dest.begin(), FAccessor(), 3.0);

Required Interface:

        RandomAccessIterator is, isend;
        RandomAccessIterator id;

        SrcAccessor src_accessor;
        DestAccessor dest_accessor;

        NumericTraits<SrcAccessor::value_type>::RealPromote s = src_accessor(is);
        double d;

        s = s + s;
        s = s - s;
        s = d * s;

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

Preconditions:

        scale > 0

void vigra::recursiveFilterX | ( |  | ... | ) |

Performs 1 dimensional recursive filtering (1st and 2nd order) in x direction.

It calls recursiveFilterLine() for every row of the image. See recursiveFilterLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            // first order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterX(SrcImageIterator supperleft,
                                   SrcImageIterator slowerright, SrcAccessor as,
                                   DestImageIterator dupperleft, DestAccessor ad,
                                   double b, BorderTreatmentMode border);

            // second order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterX(SrcImageIterator supperleft,
                                   SrcImageIterator slowerright, SrcAccessor as,
                                   DestImageIterator dupperleft, DestAccessor ad,
                                   double b1, double b2);
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            // first order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterX(
                        triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                        pair<DestImageIterator, DestAccessor> dest,
                        double b, BorderTreatmentMode border);

            // second order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterX(
                        triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                        pair<DestImageIterator, DestAccessor> dest,
                        double b1, double b2);
                }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveSmoothX(srcImageRange(src), destImage(dest),
               0.5, BORDER_TREATMENT_REFLECT);

void vigra::recursiveGaussianFilterX | ( |  | ... | ) |

Compute 1 dimensional recursive approximation of Gaussian smoothing in y direction.

It calls recursiveGaussianFilterLine() for every column of the image. See recursiveGaussianFilterLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void
            recursiveGaussianFilterX(SrcImageIterator supperleft, SrcImageIterator slowerright, SrcAccessor as,
                                     DestImageIterator dupperleft, DestAccessor ad,
                                     double sigma);
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void
            recursiveGaussianFilterX(triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                                     pair<DestImageIterator, DestAccessor> dest,
                                     double sigma);
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveGaussianFilterX(srcImageRange(src), destImage(dest), 3.0);

void vigra::recursiveSmoothX | ( |  | ... | ) |

Performs 1 dimensional recursive smoothing in x direction.

It calls recursiveSmoothLine() for every row of the image. See recursiveSmoothLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSmoothX(SrcImageIterator supperleft,
                      SrcImageIterator slowerright, SrcAccessor as,
                      DestImageIterator dupperleft, DestAccessor ad,
                      double scale)
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSmoothX(
                triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                pair<DestImageIterator, DestAccessor> dest,
                double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveSmoothX(srcImageRange(src), destImage(dest), 3.0);

Examples:
  smooth.cxx, and watershed.cxx.

void vigra::recursiveFilterY | ( |  | ... | ) |

Performs 1 dimensional recursive filtering (1st and 2nd order) in y direction.

It calls recursiveFilterLine() for every column of the image. See recursiveFilterLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            // first order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterY(SrcImageIterator supperleft,
                                   SrcImageIterator slowerright, SrcAccessor as,
                                   DestImageIterator dupperleft, DestAccessor ad,
                                   double b, BorderTreatmentMode border);

            // second order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterY(SrcImageIterator supperleft,
                                   SrcImageIterator slowerright, SrcAccessor as,
                                   DestImageIterator dupperleft, DestAccessor ad,
                                   double b1, double b2);
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            // first order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterY(
                        triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                        pair<DestImageIterator, DestAccessor> dest,
                        double b, BorderTreatmentMode border);

            // second order filter
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void recursiveFilterY(
                        triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                        pair<DestImageIterator, DestAccessor> dest,
                        double b1, double b2);
                }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveFilterY(srcImageRange(src), destImage(dest), -0.6, -0.06);

void vigra::recursiveGaussianFilterY | ( |  | ... | ) |

Compute 1 dimensional recursive approximation of Gaussian smoothing in y direction.

It calls recursiveGaussianFilterLine() for every column of the image. See recursiveGaussianFilterLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void
            recursiveGaussianFilterY(SrcImageIterator supperleft, SrcImageIterator slowerright, SrcAccessor as,
                                     DestImageIterator dupperleft, DestAccessor ad,
                                     double sigma);
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                      class DestImageIterator, class DestAccessor>
            void
            recursiveGaussianFilterY(triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                                     pair<DestImageIterator, DestAccessor> dest,
                                     double sigma);
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveGaussianFilterY(srcImageRange(src), destImage(dest), 3.0);

void vigra::recursiveSmoothY | ( |  | ... | ) |

Performs 1 dimensional recursive smoothing in y direction.

It calls recursiveSmoothLine() for every column of the image. See recursiveSmoothLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSmoothY(SrcImageIterator supperleft,
                      SrcImageIterator slowerright, SrcAccessor as,
                      DestImageIterator dupperleft, DestAccessor ad,
                      double scale)
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSmoothY(
                triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                pair<DestImageIterator, DestAccessor> dest,
                double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveSmoothY(srcImageRange(src), destImage(dest), 3.0);

Examples:
  smooth.cxx, and watershed.cxx.

void vigra::recursiveFirstDerivativeX | ( |  | ... | ) |

Recursively calculates the 1 dimensional first derivative in x direction.

It calls recursiveFirstDerivativeLine() for every row of the image. See recursiveFirstDerivativeLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveFirstDerivativeX(SrcImageIterator supperleft,
                      SrcImageIterator slowerright, SrcAccessor as,
                      DestImageIterator dupperleft, DestAccessor ad,
                      double scale)
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveFirstDerivativeX(
                triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                pair<DestImageIterator, DestAccessor> dest,
                double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveFirstDerivativeX(srcImageRange(src), destImage(dest), 3.0);

Examples:
  watershed.cxx.

void vigra::recursiveFirstDerivativeY | ( |  | ... | ) |

Recursively calculates the 1 dimensional first derivative in y direction.

It calls recursiveFirstDerivativeLine() for every column of the image. See recursiveFirstDerivativeLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveFirstDerivativeY(SrcImageIterator supperleft,
                      SrcImageIterator slowerright, SrcAccessor as,
                      DestImageIterator dupperleft, DestAccessor ad,
                      double scale)
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveFirstDerivativeY(
                triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                pair<DestImageIterator, DestAccessor> dest,
                double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveFirstDerivativeY(srcImageRange(src), destImage(dest), 3.0);

Examples:
  watershed.cxx.

void vigra::recursiveSecondDerivativeX | ( |  | ... | ) |

Recursively calculates the 1 dimensional second derivative in x direction.

It calls recursiveSecondDerivativeLine() for every row of the image. See recursiveSecondDerivativeLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSecondDerivativeX(SrcImageIterator supperleft,
                      SrcImageIterator slowerright, SrcAccessor as,
                      DestImageIterator dupperleft, DestAccessor ad,
                      double scale)
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSecondDerivativeX(
                triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                pair<DestImageIterator, DestAccessor> dest,
                double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveSecondDerivativeX(srcImageRange(src), destImage(dest), 3.0);

void vigra::recursiveSecondDerivativeY | ( |  | ... | ) |

Recursively calculates the 1 dimensional second derivative in y direction.

It calls recursiveSecondDerivativeLine() for every column of the image. See recursiveSecondDerivativeLine() for more information about required interfaces and vigra_preconditions.

Declarations:

pass arguments explicitly:

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSecondDerivativeY(SrcImageIterator supperleft,
                      SrcImageIterator slowerright, SrcAccessor as,
                      DestImageIterator dupperleft, DestAccessor ad,
                      double scale)
        }

use argument objects in conjunction with Argument Object Factories :

        namespace vigra {
            template <class SrcImageIterator, class SrcAccessor,
                  class DestImageIterator, class DestAccessor>
            void recursiveSecondDerivativeY(
                triple<SrcImageIterator, SrcImageIterator, SrcAccessor> src,
                pair<DestImageIterator, DestAccessor> dest,
                double scale)
        }

Usage:

#include <vigra/recursiveconvolution.hxx>
Namespace: vigra

        vigra::FImage src(w,h), dest(w,h);
        ...

        vigra::recursiveSecondDerivativeY(srcImageRange(src), destImage(dest), 3.0);

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)