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# vigra/gradient_energy_tensor.hxx

    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*               Copyright 2004-2005 by Ullrich Koethe                  */
    00004 /*       Cognitive Systems Group, University of Hamburg, Germany        */
    00005 /*                                                                      */
    00006 /*    This file is part of the VIGRA computer vision library.           */
    00007 /*    ( Version 1.6.0, Aug 13 2008 )                                    */
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    00011 /*        ullrich.koethe@iwr.uni-heidelberg.de    or                    */
    00012 /*        vigra@informatik.uni-hamburg.de                               */
    00013 /*                                                                      */
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    00025 /*    Software.                                                         */
    00026 /*                                                                      */
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    00035 /*                                                                      */
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    00037
    00038
    00039 #ifndef VIGRA_GRADIENT_ENERGY_TENSOR_HXX
    00040 #define VIGRA_GRADIENT_ENERGY_TENSOR_HXX
    00041
    00042 #include <cmath>
    00043 #include <functional>
    00044 #include "utilities.hxx"
    00045 #include "array_vector.hxx"
    00046 #include "basicimage.hxx"
    00047 #include "combineimages.hxx"
    00048 #include "numerictraits.hxx"
    00049 #include "convolution.hxx"
    00050
    00051 namespace vigra {
    00052
    00053 /** \addtogroup TensorImaging Tensor Image Processing
    00054 */
    00055 //@{
    00056
    00057 /********************************************************/
    00058 /*                                                      */
    00059 /*                 gradientEnergyTensor                 */
    00060 /*                                                      */
    00061 /********************************************************/
    00062
    00063 /** \brief Calculate the gradient energy tensor for a scalar valued image.
    00064
    00065     These function calculates the gradient energy tensor (GET operator) as described in
    00066
    00067     M. Felsberg, U. K&ouml;the:
    00068     <i>"GET: The Connection Between Monogenic Scale-Space and Gaussian Derivatives"</i>,
    00069     in: R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale Space and PDE Methods in Computer Vision,
    00070     Proc. of Scale-Space 2005, Lecture Notes in Computer Science 3459, pp. 192-203, Heidelberg: Springer, 2005.
    00071
    00072     U. K&ouml;the, M. Felsberg:
    00073     <i>"Riesz-Transforms Versus Derivatives: On the Relationship Between the Boundary Tensor and the Energy Tensor"</i>,
    00074     in: ditto, pp. 179-191.
    00075
    00076     with the given filters: The derivative filter \a derivKernel is applied to the appropriate image dimensions
    00077     in turn (see the papers above for details), and the other dimension is smoothed with \a smoothKernel.
    00078     The kernels can be as small as 3x1, e.g. [0.5, 0, -0.5] and [3.0/16.0, 10.0/16.0, 3.0/16.0] respectively.
    00079     The output image must have 3 bands which will hold the
    00080     tensor components in the order t11, t12 (== t21), t22. The signs of the output are adjusted for a right-handed
    00081     coordinate system. Thus, orientations derived from the tensor will be in counter-clockwise (mathematically positive)
    00082     order, with the x-axis at zero degrees (this is the standard in all VIGRA functions that deal with orientation).
    00083
    00084     <b> Declarations:</b>
    00085
    00086     pass arguments explicitly:
    00087     \code
    00088     namespace vigra {
    00089         template <class SrcIterator, class SrcAccessor,
    00090                   class DestIterator, class DestAccessor>
    00091         void gradientEnergyTensor(SrcIterator supperleft, SrcIterator slowerright, SrcAccessor src,
    00092                                   DestIterator dupperleft, DestAccessor dest,
    00093                                   Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
    00094     }
    00095     \endcode
    00096
    00097     use argument objects in conjunction with \ref ArgumentObjectFactories :
    00098     \code
    00099     namespace vigra {
    00100         template <class SrcIterator, class SrcAccessor,
    00101                   class DestIterator, class DestAccessor>
    00102         void gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00103                                   pair<DestIterator, DestAccessor> dest,
    00104                                   Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
    00105     }
    00106     \endcode
    00107
    00108     <b> Usage:</b>
    00109
    00110     <b>\#include</b> <<a href="gradient__energy__tensor_8hxx-source.html">vigra/gradient_energy_tensor.hxx</a>>
    00111
    00112     \code
    00113     FImage img(w,h);
    00114     FVector3Image get(w,h);
    00115     Kernel1D<double> grad, smooth;
    00116     grad.initGaussianDerivative(0.7, 1);
    00117     smooth.initGaussian(0.7);
    00118     ...
    00119     gradientEnergyTensor(srcImageRange(img), destImage(get), grad, smooth);
    00120     \endcode
    00121
    00122 */
    00123 doxygen_overloaded_function(template <...> void gradientEnergyTensor)
    00124
    00125 template <class SrcIterator, class SrcAccessor,
    00126           class DestIterator, class DestAccessor>
    00127 void gradientEnergyTensor(SrcIterator supperleft, SrcIterator slowerright, SrcAccessor src,
    00128                           DestIterator dupperleft, DestAccessor dest,
    00129                           Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
    00130 {
    00131     vigra_precondition(dest.size(dupperleft) == 3,
    00132                        "gradientEnergyTensor(): output image must have 3 bands.");
    00133
    00134     int w = slowerright.x - supperleft.x;
    00135     int h = slowerright.y - supperleft.y;
    00136
    00137     typedef typename
    00138        NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
    00139     typedef BasicImage<TmpType> TmpImage;
    00140     TmpImage gx(w, h), gy(w, h),
    00141              gxx(w, h), gxy(w, h), gyy(w, h),
    00142              laplace(w, h), gx3(w, h), gy3(w, h);
    00143
    00144     convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gx),
    00145                   derivKernel, smoothKernel);
    00146     convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gy),
    00147                   smoothKernel, derivKernel);
    00148     convolveImage(srcImageRange(gx), destImage(gxx),
    00149                   derivKernel, smoothKernel);
    00150     convolveImage(srcImageRange(gx), destImage(gxy),
    00151                   smoothKernel, derivKernel);
    00152     convolveImage(srcImageRange(gy), destImage(gyy),
    00153                   smoothKernel, derivKernel);
    00154     combineTwoImages(srcImageRange(gxx), srcImage(gyy), destImage(laplace),
    00155                      std::plus<TmpType>());
    00156     convolveImage(srcImageRange(laplace), destImage(gx3),
    00157                   derivKernel, smoothKernel);
    00158     convolveImage(srcImageRange(laplace), destImage(gy3),
    00159                   smoothKernel, derivKernel);
    00160     typename TmpImage::iterator gxi  = gx.begin(),
    00161                                 gyi  = gy.begin(),
    00162                                 gxxi = gxx.begin(),
    00163                                 gxyi = gxy.begin(),
    00164                                 gyyi = gyy.begin(),
    00165                                 gx3i = gx3.begin(),
    00166                                 gy3i = gy3.begin();
    00167     for(int y = 0; y < h; ++y, ++dupperleft.y)
    00168     {
    00169         typename DestIterator::row_iterator d = dupperleft.rowIterator();
    00170         for(int x = 0; x < w; ++x, ++d, ++gxi, ++gyi, ++gxxi, ++gxyi, ++gyyi, ++gx3i, ++gy3i)
    00171         {
    00172             dest.setComponent(sq(*gxxi) + sq(*gxyi) - *gxi * *gx3i, d, 0);
    00173             dest.setComponent(- *gxyi * (*gxxi + *gyyi) + 0.5 * (*gxi * *gy3i + *gyi * *gx3i), d, 1);
    00174             dest.setComponent(sq(*gxyi) + sq(*gyyi) - *gyi * *gy3i, d, 2);
    00175         }
    00176     }
    00177 }
    00178
    00179 template <class SrcIterator, class SrcAccessor,
    00180           class DestIterator, class DestAccessor>
    00181 inline
    00182 void gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00183                           pair<DestIterator, DestAccessor> dest,
    00184                           Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
    00185 {
    00186     gradientEnergyTensor(src.first, src.second, src.third,
    00187                          dest.first, dest.second, derivKernel, smoothKernel);
    00188 }
    00189
    00190 //@}
    00191
    00192 } // namespace vigra
    00193
    00194 #endif // VIGRA_GRADIENT_ENERGY_TENSOR_HXX

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python VIGRA 1.6.0 (13 Aug 2008)