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gradient_energy_tensor.hxx
1/************************************************************************/
2/* */
3/* Copyright 2004-2005 by Ullrich Koethe */
4/* */
5/* This file is part of the VIGRA computer vision library. */
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9/* ullrich.koethe@iwr.uni-heidelberg.de or */
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35
36
37#ifndef VIGRA_GRADIENT_ENERGY_TENSOR_HXX
38#define VIGRA_GRADIENT_ENERGY_TENSOR_HXX
39
40#include <cmath>
41#include <functional>
42#include "utilities.hxx"
43#include "array_vector.hxx"
44#include "basicimage.hxx"
45#include "combineimages.hxx"
46#include "numerictraits.hxx"
47#include "convolution.hxx"
48#include "multi_shape.hxx"
49
50namespace vigra {
51
52/** \addtogroup TensorImaging Tensor Image Processing
53*/
54//@{
55
56/********************************************************/
57/* */
58/* gradientEnergyTensor */
59/* */
60/********************************************************/
61
62/** \brief Calculate the gradient energy tensor for a scalar valued image.
63
64 These function calculates the gradient energy tensor (GET operator) as described in
65
66 M. Felsberg, U. K&ouml;the:
67 <i>"GET: The Connection Between Monogenic Scale-Space and Gaussian Derivatives"</i>,
68 in: R. Kimmel, N. Sochen, J. Weickert (Eds.): Scale Space and PDE Methods in Computer Vision,
69 Proc. of Scale-Space 2005, Lecture Notes in Computer Science 3459, pp. 192-203, Heidelberg: Springer, 2005.
70
71 U. K&ouml;the, M. Felsberg:
72 <i>"Riesz-Transforms Versus Derivatives: On the Relationship Between the Boundary Tensor and the Energy Tensor"</i>,
73 in: ditto, pp. 179-191.
74
75 with the given filters: The derivative filter \a derivKernel is applied to the appropriate image dimensions
76 in turn (see the papers above for details), and the other dimension is smoothed with \a smoothKernel.
77 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.
78 The output image must have 3 bands which will hold the
79 tensor components in the order t11, t12 (== t21), t22. The signs of the output are adjusted for a right-handed
80 coordinate system. Thus, orientations derived from the tensor will be in counter-clockwise (mathematically positive)
81 order, with the x-axis at zero degrees (this is the standard in all VIGRA functions that deal with orientation).
82
83 <b> Declarations:</b>
84
85 pass 2D array views:
86 \code
87 namespace vigra {
88 template <class T1, class S1,
89 class T2, class S2>
90 void
91 gradientEnergyTensor(MultiArrayView<2, T1, S1> const & src,
92 MultiArrayView<2, T2, S2> dest,
93 Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
94 }
95 \endcode
96
97 \deprecatedAPI{gradientEnergyTensor}
98 pass \ref ImageIterators and \ref DataAccessors :
99 \code
100 namespace vigra {
101 template <class SrcIterator, class SrcAccessor,
102 class DestIterator, class DestAccessor>
103 void gradientEnergyTensor(SrcIterator supperleft, SrcIterator slowerright, SrcAccessor src,
104 DestIterator dupperleft, DestAccessor dest,
105 Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
106 }
107 \endcode
108 use argument objects in conjunction with \ref ArgumentObjectFactories :
109 \code
110 namespace vigra {
111 template <class SrcIterator, class SrcAccessor,
112 class DestIterator, class DestAccessor>
113 void gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
114 pair<DestIterator, DestAccessor> dest,
115 Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel);
116 }
117 \endcode
118 \deprecatedEnd
119
120 <b> Usage:</b>
121
122 <b>\#include</b> <vigra/gradient_energy_tensor.hxx><br/>
123 Namespace: vigra
124
125 \code
126 MultiArray<2, float> img(w,h);
127 MultiArray<2, TinyVector<float, 3> > get(w,h);
128 Kernel1D<double> grad, smooth;
129 grad.initGaussianDerivative(0.7, 1);
130 smooth.initGaussian(0.7);
131 ...
132 gradientEnergyTensor(img, get, grad, smooth);
133 \endcode
134
135 \deprecatedUsage{gradientEnergyTensor}
136 \code
137 FImage img(w,h);
138 FVector3Image get(w,h);
139 Kernel1D<double> grad, smooth;
140 grad.initGaussianDerivative(0.7, 1);
141 smooth.initGaussian(0.7);
142 ...
143 gradientEnergyTensor(srcImageRange(img), destImage(get), grad, smooth);
144 \endcode
145 \deprecatedEnd
146*/
147doxygen_overloaded_function(template <...> void gradientEnergyTensor)
148
149template <class SrcIterator, class SrcAccessor,
150 class DestIterator, class DestAccessor>
154{
155 vigra_precondition(dest.size(dupperleft) == 3,
156 "gradientEnergyTensor(): output image must have 3 bands.");
157
158 int w = slowerright.x - supperleft.x;
159 int h = slowerright.y - supperleft.y;
160
161 typedef typename
162 NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
164 TmpImage gx(w, h), gy(w, h),
165 gxx(w, h), gxy(w, h), gyy(w, h),
166 laplace(w, h), gx3(w, h), gy3(w, h);
167
168 convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gx),
170 convolveImage(srcIterRange(supperleft, slowerright, src), destImage(gy),
172 convolveImage(srcImageRange(gx), destImage(gxx),
174 convolveImage(srcImageRange(gx), destImage(gxy),
176 convolveImage(srcImageRange(gy), destImage(gyy),
178 combineTwoImages(srcImageRange(gxx), srcImage(gyy), destImage(laplace),
179 std::plus<TmpType>());
180 convolveImage(srcImageRange(laplace), destImage(gx3),
182 convolveImage(srcImageRange(laplace), destImage(gy3),
184 typename TmpImage::iterator gxi = gx.begin(),
185 gyi = gy.begin(),
186 gxxi = gxx.begin(),
187 gxyi = gxy.begin(),
188 gyyi = gyy.begin(),
189 gx3i = gx3.begin(),
190 gy3i = gy3.begin();
191 for(int y = 0; y < h; ++y, ++dupperleft.y)
192 {
193 typename DestIterator::row_iterator d = dupperleft.rowIterator();
194 for(int x = 0; x < w; ++x, ++d, ++gxi, ++gyi, ++gxxi, ++gxyi, ++gyyi, ++gx3i, ++gy3i)
195 {
196 dest.setComponent(sq(*gxxi) + sq(*gxyi) - *gxi * *gx3i, d, 0);
197 dest.setComponent(- *gxyi * (*gxxi + *gyyi) + 0.5 * (*gxi * *gy3i + *gyi * *gx3i), d, 1);
198 dest.setComponent(sq(*gxyi) + sq(*gyyi) - *gyi * *gy3i, d, 2);
199 }
200 }
201}
202
203template <class SrcIterator, class SrcAccessor,
204 class DestIterator, class DestAccessor>
205inline void
206gradientEnergyTensor(triple<SrcIterator, SrcIterator, SrcAccessor> src,
207 pair<DestIterator, DestAccessor> dest,
208 Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
209{
210 gradientEnergyTensor(src.first, src.second, src.third,
211 dest.first, dest.second, derivKernel, smoothKernel);
212}
213
214template <class T1, class S1,
215 class T2, class S2>
216inline void
217gradientEnergyTensor(MultiArrayView<2, T1, S1> const & src,
218 MultiArrayView<2, T2, S2> dest,
219 Kernel1D<double> const & derivKernel, Kernel1D<double> const & smoothKernel)
220{
221 vigra_precondition(src.shape() == dest.shape(),
222 "gradientEnergyTensor(): shape mismatch between input and output.");
223 gradientEnergyTensor(srcImageRange(src),
224 destImage(dest), derivKernel, smoothKernel);
225}
226
227//@}
228
229} // namespace vigra
230
231#endif // VIGRA_GRADIENT_ENERGY_TENSOR_HXX
Class for a single RGB value.
Definition rgbvalue.hxx:128
size_type size() const
Definition tinyvector.hxx:913
iterator begin()
Definition tinyvector.hxx:861
void combineTwoImages(...)
Combine two source images into destination image.
void convolveImage(...)
Convolve an image with the given kernel(s).
void gradientEnergyTensor(...)
Calculate the gradient energy tensor for a scalar valued image.

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

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