Commit d85fb29b by Julien Michel

### DOC: Enhancing documentation of the Dimension Reduction chapter

parent cdc24265
 ... ... @@ -31,10 +31,15 @@ // // This example illustrates the use of the // \doxygen{otb}{FastICAImageFilter}. // This filter computes a Principal Component Analysis using an // efficient method based on the inner product in order to compute the // covariance matrix. // This filter computes a Fast Independant Components Analysis transform. // // Like Principal Components Analysis, FastICA computes a set of // orthogonal linear combinations, but the criterion of Fast ICA is // different: instead of maximizing variance, it tries to maximize // stastistical independance between components. In Fast ICA, // statistical independance is mesured by evaluating non-Gaussianity // of the components, and the maximization is done in an iterative way. // The first step required to use this filter is to include its header file. // // Software Guide : EndLatex ... ... @@ -144,7 +149,7 @@ int main(int argc, char* argv[]) // Software Guide : EndCodeSnippet // Software Guide : BeginLatex // // We finally plug the pipeline and trigger the PCA computation with // We finally plug the pipeline and trigger the ICA computation with // the method \code{Update()} of the writer. // // Software Guide : EndLatex ... ... @@ -159,7 +164,7 @@ int main(int argc, char* argv[]) // Software Guide : BeginLatex // // \doxygen{otb}{FastICAImageFilter} allows also to compute inverse // transformation from PCA coefficients. In reverse mode, the // transformation from ICA coefficients. In reverse mode, the // covariance matrix or the transformation matrix // (which may not be square) has to be given. // ... ... @@ -173,7 +178,8 @@ int main(int argc, char* argv[]) invFilter->SetMeanValues( FastICAfilter->GetMeanValues() ); invFilter->SetStdDevValues( FastICAfilter->GetStdDevValues() ); invFilter->SetTransformationMatrix( FastICAfilter->GetTransformationMatrix() ); invFilter->SetPCATransformationMatrix( FastICAfilter->GetPCATransformationMatrix() ); invFilter->SetPCATransformationMatrix( FastICAfilter->GetPCATransformationMatrix() ); invFilter->SetInput(FastICAfilter->GetOutput()); WriterType::Pointer invWriter = WriterType::New(); ... ...
 ... ... @@ -30,11 +30,22 @@ // Software Guide : BeginLatex // // This example illustrates the use of the // \doxygen{otb}{MNFImageFilter}. // This filter computes a Principal Component Analysis using an // \doxygen{otb}{MNFImageFilter}. This filter computes a Minimum // Noise Fraction transform \cite{nielsen2011kernel} using an // efficient method based on the inner product in order to compute the // covariance matrix. // // The Minimum Noise Fraction transform is a sequence of two Principal // Components Analysis transform. The first transform is based on an // estimated covariance matrix of the noise, and intends to whiten the // input image (noise with unit variance and no correlation between // bands). // // The second Principal Components Analysis is then applied to the // noise-whitened image, giving the Minimum Noise Fraction transform. // // In this implementation, noise is estimated from a local window. // // The first step required to use this filter is to include its header file. // // Software Guide : EndLatex ... ... @@ -100,7 +111,7 @@ int main(int argc, char* argv[]) // Software Guide : EndLatex // SoftwareGuide : BeginCodeSnippet typedef otb::LocalActivityVectorImageFilter< ImageType, ImageType > NoiseFilterType; typedef otb::LocalActivityVectorImageFilter NoiseFilterType; // SoftwareGuide : EndCodeSnippet ... ... @@ -167,7 +178,7 @@ int main(int argc, char* argv[]) // Software Guide : EndCodeSnippet // Software Guide : BeginLatex // // We finally plug the pipeline and trigger the PCA computation with // We finally plug the pipeline and trigger the MNF computation with // the method \code{Update()} of the writer. // // Software Guide : EndLatex ... ... @@ -182,7 +193,7 @@ int main(int argc, char* argv[]) // Software Guide : BeginLatex // // \doxygen{otb}{MNFImageFilter} allows also to compute inverse // transformation from PCA coefficients. In reverse mode, the // transformation from MNF coefficients. In reverse mode, the // covariance matrix or the transformation matrix // (which may not be square) has to be given. // ... ...
 ... ... @@ -28,7 +28,16 @@ // Software Guide : BeginLatex // This example illustrates the class // \doxygen{otb}{MaximumAutocorrelationFactorImageFilter} ... // \doxygen{otb}{MaximumAutocorrelationFactorImageFilter}, which // performs a Maximum Autocorrelation Factor transform \cite{nielsen2011kernel}. Like // PCA, MAF tries to find a set of orthogonal linear transform, but // the criterion to maximize is the auto-correlation rather than the // variance. // // Auto-correlation is the correlation between the component and a // unitary shifted version of the component. // // Please note that the inverse transform is not implemented yet. // // We start by including the corresponding header file. // ... ...
 ... ... @@ -100,7 +100,7 @@ int main(int argc, char* argv[]) // Software Guide : EndLatex // SoftwareGuide : BeginCodeSnippet typedef otb::LocalActivityVectorImageFilter< ImageType, ImageType > NoiseFilterType; typedef otb::LocalActivityVectorImageFilter NoiseFilterType; // SoftwareGuide : EndCodeSnippet ... ... @@ -167,7 +167,7 @@ int main(int argc, char* argv[]) // Software Guide : EndCodeSnippet // Software Guide : BeginLatex // // We finally plug the pipeline and trigger the PCA computation with // We finally plug the pipeline and trigger the NA-PCA computation with // the method \code{Update()} of the writer. // // Software Guide : EndLatex ... ... @@ -182,7 +182,7 @@ int main(int argc, char* argv[]) // Software Guide : BeginLatex // // \doxygen{otb}{NAPCAImageFilter} allows also to compute inverse // transformation from PCA coefficients. In reverse mode, the // transformation from NA-PCA coefficients. In reverse mode, the // covariance matrix or the transformation matrix // (which may not be square) has to be given. // ... ... @@ -209,7 +209,7 @@ int main(int argc, char* argv[]) // Software Guide : BeginLatex // Figure~\ref{fig:NAPCA_FILTER} shows the result of applying forward // and reverse NAPCA transformation to a 8 bands Wordlview2 image. // and reverse NA-PCA transformation to a 8 bands Wordlview2 image. // \begin{figure} // \center // \includegraphics[width=0.32\textwidth]{napca-input-pretty.eps} ... ...
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