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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.           */
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00011 /*        vigra@informatik.uni-hamburg.de                               */
00012 /*                                                                      */
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00024 /*    Software.                                                         */
00025 /*                                                                      */
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00036 
00037 
00038 #ifndef VIGRA_GRADIENT_ENERGY_TENSOR_HXX
00039 #define VIGRA_GRADIENT_ENERGY_TENSOR_HXX
00040 
00041 #include <cmath>
00042 #include <functional>
00043 #include "utilities.hxx"
00044 #include "array_vector.hxx"
00045 #include "basicimage.hxx"
00046 #include "combineimages.hxx"
00047 #include "numerictraits.hxx"
00048 #include "convolution.hxx"
00049 
00050 namespace vigra {
00051 
00052 /** \addtogroup TensorImaging Tensor Image Processing
00053 */
00054 //@{
00055 
00056 /********************************************************/
00057 /*                                                      */
00058 /*                 gradientEnergyTensor                 */
00059 /*                                                      */
00060 /********************************************************/
00061 
00062 /** \brief Calculate the gradient energy tensor for a scalar valued image.
00063 
00064     These function calculates the gradient energy tensor (GET operator) as described in
00065     
00066     M. Felsberg, U. K&ouml;the: 
00067     <i>"GET: The Connection Between Monogenic Scale-Space and Gaussian Derivatives"</i>, 
00068     in: R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale Space and PDE Methods in Computer Vision, 
00069     Proc. of Scale-Space 2005, Lecture Notes in Computer Science 3459, pp. 192-203, Heidelberg: Springer, 2005.
00070     
00071     U. K&ouml;the, M. Felsberg: 
00072     <i>"Riesz-Transforms Versus Derivatives: On the Relationship Between the Boundary Tensor and the Energy Tensor"</i>, 
00073     in: ditto, pp. 179-191.
00074 
00075     with the given filters: The derivative filter \a derivKernel is applied to the appropriate image dimensions 
00076     in turn (see the papers above for details), and the other dimension is smoothed with \a smoothKernel. 
00077     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.  
00078     The output image must have 3 bands which will hold the
00079     tensor components in the order t11, t12 (== t21), t22. The signs of the output are adjusted for a right-handed
00080     coordinate system. Thus, orientations derived from the tensor will be in counter-clockwise (mathematically positive)
00081     order, with the x-axis at zero degrees (this is the standard in all VIGRA functions that deal with orientation).
00082     
00083     <b> Declarations:</b>
00084 
00085     pass arguments explicitly:
00086     \code
00087     namespace vigra {
00088         template <class SrcIterator, class SrcAccessor,
00089                   class DestIterator, class DestAccessor>
00090         void gradientEnergyTensor(SrcIterator supperleft, SrcIterator slowerright, SrcAccessor src,
00091                                   DestIterator dupperleft, DestAccessor dest,
00092                                   Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
00093     }
00094     \endcode
00095 
00096     use argument objects in conjunction with \ref ArgumentObjectFactories :
00097     \code
00098     namespace vigra {
00099         template <class SrcIterator, class SrcAccessor,
00100                   class DestIterator, class DestAccessor>
00101         void gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
00102                                   pair<DestIterator, DestAccessor> dest,
00103                                   Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
00104     }
00105     \endcode
00106 
00107     <b> Usage:</b>
00108 
00109     <b>\#include</b> <<a href="gradient__energy__tensor_8hxx-source.html">vigra/gradient_energy_tensor.hxx</a>>
00110 
00111     \code
00112     FImage img(w,h);
00113     FVector3Image get(w,h);
00114     Kernel1D<double> grad, smooth;
00115     grad.initGaussianDerivative(0.7, 1);
00116     smooth.initGaussian(0.7);
00117     ...
00118     gradientEnergyTensor(srcImageRange(img), destImage(get), grad, smooth);
00119     \endcode
00120 
00121 */
00122 doxygen_overloaded_function(template <...> void gradientEnergyTensor)
00123 
00124 template <class SrcIterator, class SrcAccessor,
00125           class DestIterator, class DestAccessor>
00126 void gradientEnergyTensor(SrcIterator supperleft, SrcIterator slowerright, SrcAccessor src,
00127                           DestIterator dupperleft, DestAccessor dest,
00128                           Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
00129 {
00130     vigra_precondition(dest.size(dupperleft) == 3,
00131                        "gradientEnergyTensor(): output image must have 3 bands.");
00132 
00133     int w = slowerright.x - supperleft.x;
00134     int h = slowerright.y - supperleft.y;
00135     
00136     typedef typename 
00137        NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
00138     typedef BasicImage<TmpType> TmpImage;    
00139     TmpImage gx(w, h), gy(w, h), 
00140              gxx(w, h), gxy(w, h), gyy(w, h), 
00141              laplace(w, h), gx3(w, h), gy3(w, h);
00142     
00143     convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gx), 
00144                   derivKernel, smoothKernel);
00145     convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gy), 
00146                   smoothKernel, derivKernel);
00147     convolveImage(srcImageRange(gx), destImage(gxx), 
00148                   derivKernel, smoothKernel);
00149     convolveImage(srcImageRange(gx), destImage(gxy), 
00150                   smoothKernel, derivKernel);
00151     convolveImage(srcImageRange(gy), destImage(gyy), 
00152                   smoothKernel, derivKernel);
00153     combineTwoImages(srcImageRange(gxx), srcImage(gyy), destImage(laplace), 
00154                      std::plus<TmpType>());
00155     convolveImage(srcImageRange(laplace), destImage(gx3), 
00156                   derivKernel, smoothKernel);
00157     convolveImage(srcImageRange(laplace), destImage(gy3), 
00158                   smoothKernel, derivKernel);
00159     typename TmpImage::iterator gxi  = gx.begin(),
00160                                 gyi  = gy.begin(),
00161                                 gxxi = gxx.begin(),
00162                                 gxyi = gxy.begin(),
00163                                 gyyi = gyy.begin(),
00164                                 gx3i = gx3.begin(),
00165                                 gy3i = gy3.begin();
00166     for(int y = 0; y < h; ++y, ++dupperleft.y)
00167     {
00168         typename DestIterator::row_iterator d = dupperleft.rowIterator(); 
00169         for(int x = 0; x < w; ++x, ++d, ++gxi, ++gyi, ++gxxi, ++gxyi, ++gyyi, ++gx3i, ++gy3i)
00170         {
00171             dest.setComponent(sq(*gxxi) + sq(*gxyi) - *gxi * *gx3i, d, 0);
00172             dest.setComponent(- *gxyi * (*gxxi + *gyyi) + 0.5 * (*gxi * *gy3i + *gyi * *gx3i), d, 1);
00173             dest.setComponent(sq(*gxyi) + sq(*gyyi) - *gyi * *gy3i, d, 2);
00174         }
00175     }
00176 }
00177 
00178 template <class SrcIterator, class SrcAccessor,
00179           class DestIterator, class DestAccessor>
00180 inline
00181 void gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
00182                           pair<DestIterator, DestAccessor> dest,
00183                           Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
00184 {
00185     gradientEnergyTensor(src.first, src.second, src.third,
00186                          dest.first, dest.second, derivKernel, smoothKernel);
00187 }
00188 
00189 //@}
00190 
00191 } // namespace vigra
00192 
00193 #endif // VIGRA_GRADIENT_ENERGY_TENSOR_HXX

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de)
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

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VIGRA 1.6.0 (5 Nov 2009)