Commit 611eed98 by Manuel Grizonnet

### DOC: all haralick formulas in the doxygen and add link in SG and haralick app

parent 9f88f60b
 ... ... @@ -11,60 +11,34 @@ etc. They can also be measures : moments, textures, etc. \subsection{Haralick Descriptors} This example illustrates the use of the \doxygen{otb}{ScalarImageToTexturesFilter}, which compute the standard Haralick's textural features~\cite{Haralick1973} presented in table~\ref{tab:haralickStandardFeatures}, where $\mu_t$ and $\sigma_t$ are the mean and standard deviation of the row (or column, due to symmetry) sums, $\mu =$ (weighted pixel average) $= \sum_{i,j}i \cdot g(i, j) =\sum_{i,j}j \cdot g(i, j)$ due to matrix summetry, and $\sigma =$ (weighted pixel variance) $= \sum_{i,j}(i - \mu)^2 \cdot g(i, j) =\sum_{i,j}(j - \mu)^2 \cdot g(i, j)$ due to matrix symmetry. \begin{table} \begin{center} \begin{tabular}{|c|c|} \hline & \\ Energy & $f_1 = \sum_{i,j}g(i, j)^2$ \\ & \\ \hline & \\ Entropy & $f_2 = -\sum_{i,j}g(i, j) \log_2 g(i, j)$, or 0 if $g(i, j) = 0$ \\ & \\ \hline & \\ Correlation & $f_3 = \sum_{i,j}\frac{(i - \mu)(j - \mu)g(i, j)}{\sigma^2}$ \\ & \\ \hline & \\ Difference Moment & $f_4 = \sum_{i,j}\frac{1}{1 + (i - j)^2}g(i, j)$ \\ & \\ \hline & \\ Inertia (a.k.a. Contrast) & $f_5 = \sum_{i,j}(i - j)^2g(i, j)$ \\ & \\ \hline & \\ Cluster Shade & $f_6 = \sum_{i,j}((i - \mu) + (j - \mu))^3 g(i, j)$ \\ & \\ \hline Cluster Prominence & $f_7 = \sum_{i,j}((i - \mu) + (j - \mu))^4 g(i, j)$ \\ & \\ \hline & \\ Haralick's Correlation & $f_8 = \frac{\sum_{i,j}(i, j) g(i, j) -\mu_t^2}{\sigma_t^2}$ \\ & \\ \hline \end{tabular} \itkcaption[Haralick features]{Haralick features~\cite{Haralick1973} available in \doxygen{otb}{ScalarImageToTexturesFilter}} \end{center} \label{tab:haralickStandardFeatures} \end{table} More features are available in \doxygen{otb}{ScalarImageToAdvancedTexturesFilter}. which computes the standard Haralick's textural features~\cite{Haralick1973}. The \doxygen{otb}{ScalarImageToTexturesFilter} class computes 8 local Haralick textures features. Their formulas are available in the \doxygen{otb}{ScalarImageToTexturesFilter} documentation (see section \textit{Detailed Description} in the doxygen). Note that more features are available in \doxygen{otb}{ScalarImageToAdvancedTexturesFilter} and in \doxygen{otb}{ScalarImageToHigherOrderTexturesFilter}. \doxygen{otb}{ScalarImageToHigherOrderTexturesFilter} computes 10 advanced texture features. Their formulas are available in the \doxygen{otb}{ScalarImageToAdvancedTexturesFilter} documentation (see section \textit{Detailed Description} in the doxygen). \doxygen{otb}{ScalarImageToHigherOrderTexturesFilter} computes 11 other local higher order statistics textures coefficients also based on the grey level run-length matrix. Formulas for these coefficients are also available in the doxygen documentation of the filter (see section \textit{Detailed Description} in the doxygen). \relatedClasses \begin{itemize} \item \doxygen{otb}{ScalarImageToAdvancedTexturesFilter} \item \doxygen{otb}{ScalarImageToPanTexTextureFilter} \item \doxygen{otb}{GreyLevelCooccurrenceIndexedList} \item \doxygen{otb}{ScalarImageToHigherOrderTexturesFilter} \end{itemize} \input{TextureExample} ... ...
 ... ... @@ -24,6 +24,7 @@ #include "otbGreyLevelCooccurrenceIndexedList.h" #include "itkMacro.h" #include "itkImageToImageFilter.h" namespace otb { /** ... ...
 ... ... @@ -64,13 +64,13 @@ namespace otb * (or column, due to symmetry) sums. * * Above, \f$\mu = \f$ (weighted pixel average) \f$= \sum_{i, j}i \cdot g(i, j) = * \sum_{i, j}j \cdot g(i, j) \f$ (due to matrix summetry), and * \sum_{i, j}j \cdot g(i, j) \f$(due to matrix symmetry), and * * \f$ \sigma = \f$(weighted pixel variance) \f$ = \sum_{i, j}(i - \mu)^2 \cdot g(i, j) = * \sum_{i, j}(j - \mu)^2 \cdot g(i, j) \f$(due to matrix summetry) * \sum_{i, j}(j - \mu)^2 \cdot g(i, j) \f$ (due to matrix symmetry) * * Print references: * References: * * Haralick, R.M., K. Shanmugam and I. Dinstein. 1973. Textural Features for * Image Classification. IEEE Transactions on Systems, Man and Cybernetics. ... ...
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