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| vigra/gradient_energy_tensor.hxx |
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    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 1.4.0, Dec 21 2005 )                                    */
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    00011 /*        koethe@informatik.uni-hamburg.de          or                  */
    00012 /*        vigra@kogs1.informatik.uni-hamburg.de                         */
    00013 /*                                                                      */
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    00022 /*                                                                      */
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    00025 /*    Software.                                                         */
    00026 /*                                                                      */
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    00035 /*                                                                      */
    00036 /************************************************************************/
    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 "vigra/utilities.hxx"
    00045 #include "vigra/array_vector.hxx"
    00046 #include "vigra/basicimage.hxx"
    00047 #include "vigra/combineimages.hxx"
    00048 #include "vigra/numerictraits.hxx"
    00049 #include "vigra/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 template <class SrcIterator, class SrcAccessor,
    00124           class DestIterator, class DestAccessor>
    00125 void gradientEnergyTensor(SrcIterator supperleft, SrcIterator slowerright, SrcAccessor src,
    00126                           DestIterator dupperleft, DestAccessor dest,
    00127                           Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
    00128 {
    00129     vigra_precondition(dest.size(dupperleft) == 3,
    00130                        "gradientEnergyTensor(): output image must have 3 bands.");
    00131
    00132     int w = slowerright.x - supperleft.x;
    00133     int h = slowerright.y - supperleft.y;
    00134
    00135     typedef typename
    00136        NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
    00137     typedef BasicImage<TmpType> TmpImage;
    00138     TmpImage gx(w, h), gy(w, h),
    00139              gxx(w, h), gxy(w, h), gyy(w, h),
    00140              laplace(w, h), gx3(w, h), gy3(w, h);
    00141
    00142     convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gx),
    00143                   derivKernel, smoothKernel);
    00144     convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gy),
    00145                   smoothKernel, derivKernel);
    00146     convolveImage(srcImageRange(gx), destImage(gxx),
    00147                   derivKernel, smoothKernel);
    00148     convolveImage(srcImageRange(gx), destImage(gxy),
    00149                   smoothKernel, derivKernel);
    00150     convolveImage(srcImageRange(gy), destImage(gyy),
    00151                   smoothKernel, derivKernel);
    00152     combineTwoImages(srcImageRange(gxx), srcImage(gyy), destImage(laplace),
    00153                      std::plus<TmpType>());
    00154     convolveImage(srcImageRange(laplace), destImage(gx3),
    00155                   derivKernel, smoothKernel);
    00156     convolveImage(srcImageRange(laplace), destImage(gy3),
    00157                   smoothKernel, derivKernel);
    00158     typename TmpImage::iterator gxi  = gx.begin(),
    00159                                 gyi  = gy.begin(),
    00160                                 gxxi = gxx.begin(),
    00161                                 gxyi = gxy.begin(),
    00162                                 gyyi = gyy.begin(),
    00163                                 gx3i = gx3.begin(),
    00164                                 gy3i = gy3.begin();
    00165     for(int y = 0; y < h; ++y, ++dupperleft.y)
    00166     {
    00167         typename DestIterator::row_iterator d = dupperleft.rowIterator();
    00168         for(int x = 0; x < w; ++x, ++d, ++gxi, ++gyi, ++gxxi, ++gxyi, ++gyyi, ++gx3i, ++gy3i)
    00169         {
    00170             dest.setComponent(sq(*gxxi) + sq(*gxyi) - *gxi * *gx3i, d, 0);
    00171             dest.setComponent(- *gxyi * (*gxxi + *gyyi) + 0.5 * (*gxi * *gy3i + *gyi * *gx3i), d, 1);
    00172             dest.setComponent(sq(*gxyi) + sq(*gyyi) - *gyi * *gy3i, d, 2);
    00173         }
    00174     }
    00175 }
    00176
    00177 template <class SrcIterator, class SrcAccessor,
    00178           class DestIterator, class DestAccessor>
    00179 inline
    00180 void gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00181                           pair<DestIterator, DestAccessor> dest,
    00182                           Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
    00183 {
    00184     gradientEnergyTensor(src.first, src.second, src.third,
    00185                          dest.first, dest.second, derivKernel, smoothKernel);
    00186 }
    00187
    00188 //@}
    00189
    00190 } // namespace vigra
    00191
    00192 #endif // VIGRA_GRADIENT_ENERGY_TENSOR_HXX

© Ullrich Köthe (koethe@informatik.uni-hamburg.de) Cognitive Systems Group, University of Hamburg, Germany | html generated using doxygen and Python VIGRA 1.4.0 (21 Dec 2005)