Commit d85fb29b authored by Julien Michel's avatar 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<ImageType,ImageType> 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<ImageType,ImageType> 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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