diff --git a/Examples/FeatureExtraction/CloudDetectionExample.cxx b/Examples/FeatureExtraction/CloudDetectionExample.cxx
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+/*=========================================================================
+
+ Program: ORFEO Toolbox
+ Language: C++
+ Date: $Date$
+ Version: $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE. See the above copyright notices for more information.
+
+=========================================================================*/
+#if defined(_MSC_VER)
+#pragma warning ( disable : 4786 )
+#endif
+
+#ifdef __BORLANDC__
+#define ITK_LEAN_AND_MEAN
+#endif
+
+// Software Guide : BeginCommandLineArgs
+// INPUTS: {CloudsOnReunion.tif}
+// OUTPUTS: {CloudDetectionOutput.png}
+// 553 467 734 581 0.25 0.8 1.0
+// Software Guide : EndCommandLineArgs
+
+
+// Software Guide : BeginLatex
+//
+// The easiest way to use the road extraction filter provided by OTB is to use the composite
+// filter. If a modification in the pipeline is required to adapt to a particular situation,
+// the step by step example, described in the next section can be adapted.
+//
+// This example demonstrates the use of the \doxygen{otb}{RoadExtractionFilter}.
+// This filter is a composite filter achieving road extraction according to the algorithm
+// adapted by E. Christophe and J. Inglada \cite{Christophe2007} from an original method
+// proposed in \cite{Lacroix1998}.
+//
+// The first step toward the use of this filter is the inclusion of the proper header files.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+#include "otbCloudDetectionFunctor.h"
+#include "otbCloudDetectionFilter.h"
+
+// Software Guide : EndCodeSnippet
+
+#include "otbImage.h"
+#include "otbVectorImage.h"
+#include "otbImageFileReader.h"
+#include "otbImageFileWriter.h"
+#include "itkExceptionObject.h"
+
+int main( int argc, char * argv[] )
+{
+
+ if (argc != 10)
+ {
+ std::cerr << "Usage: "<< argv[0];
+ std::cerr << "inputFileName outputFileName ";
+ std::cerr << "firstPixelComponent secondPixelComponent thirdPixelComponent fourthPixelComponent ";
+ std::cerr << "variance ";
+ std::cerr << "minThreshold maxThreshold "<<std::endl;
+ return EXIT_FAILURE;
+ }
+
+ const unsigned int Dimension = 2;
+ // Software Guide : BeginLatex
+ //
+ // Then we must decide what pixel type to use for the image. We choose to do
+ // all the computation in floating point precision and rescale the results
+ // between 0 and 255 in order to export PNG images.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ typedef double InputPixelType;
+ typedef unsigned short OutputPixelType;
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // The images are defined using the pixel type and the dimension. Please note that
+ // the \doxygen{otb}{RoadExtractionFilter} needs an \doxygen{otb}{VectorImage} as input
+ // to handle multispectral images.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+ typedef otb::VectorImage<InputPixelType,Dimension> VectorImageType;
+ typedef VectorImageType::PixelType VectorPixelType;
+ typedef otb::Image<OutputPixelType,Dimension> OutputImageType;
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // We define the type of the polyline that the filter produces. We use the
+ // \doxygen{otb}{PolyLineParametricPathWithValue}, which allows the filter to produce
+ // a likehood value along with each polyline. The filter is able to produce
+ // \doxygen{itk}{PolyLineParametricPath} as well.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ typedef otb::Functor::CloudDetectionFunctor<VectorPixelType,InputPixelType > FunctorType;
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // Now we can define the \doxygen{otb}{RoadExtractionFilter} that takes a multi-spectral
+ // image as input and produces a list of polylines.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ typedef otb::CloudDetectionFilter<VectorImageType,OutputImageType,FunctorType > CloudDetectionFilterType;
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // We also define an \doxygen{otb}{DrawPathListFilter} to draw the output
+ // polylines on an image, taking their likehood values into account.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ typedef otb::ImageFileReader<VectorImageType> ReaderType;
+ typedef otb::ImageFileWriter<OutputImageType> WriterType;
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // The different filters composing our pipeline are created by invoking their
+ // \code{New()} methods, assigning the results to smart pointers.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ ReaderType::Pointer reader = ReaderType::New();
+ CloudDetectionFilterType::Pointer cloudDetection = CloudDetectionFilterType::New();
+ WriterType::Pointer writer = WriterType::New();
+
+ // Software Guide : EndCodeSnippet
+
+ reader->SetFileName(argv[1]);
+ cloudDetection->SetInput(reader->GetOutput());
+
+ // Software Guide : BeginLatex
+ //
+ // The \doxygen{otb}{RoadExtractionFilter} needs to have a reference pixel
+ // corresponding to the spectral content likely to represent a road. This is done
+ // by passing a pixel to the filter. Here we suppose that the input image
+ // has four spectral bands.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ VectorPixelType referencePixel;
+ referencePixel.SetSize(4);
+ referencePixel.Fill(0.);
+ referencePixel[0] = (atof(argv[3]));
+ referencePixel[1] = (atof(argv[4]));
+ referencePixel[2] = (atof(argv[5]));
+ referencePixel[3] = (atof(argv[6]));
+ cloudDetection->SetReferencePixel(referencePixel);
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // We must also set the alpha parameter of the filter which allows us to tune the width of the roads
+ // we want to extract. Typical value is $1.0$ and should be working in most situations.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ cloudDetection->SetVariance(atof(argv[7]));
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // All other parameter should not influence the results too much in most situation and can
+ // be kept at the default value.
+ //
+ // The amplitude threshold parameter tunes the sensitivity of the vectorization step. A typical
+ // value is $5 \cdot 10^{-5}$.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ cloudDetection->SetMinThreshold(atof(argv[8]));
+ cloudDetection->SetMaxThreshold(atof(argv[9]));
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // The tolerance threshold tunes the sensitivity of the path simplification step.
+ // Typical value is $1.0$.
+ //
+ // Software Guide : EndLatex
+
+ // Software Guide : BeginCodeSnippet
+
+ writer->SetFileName(argv[2]);
+ writer->SetInput(cloudDetection->GetOutput());
+ writer->Update();
+
+ // Software Guide : EndCodeSnippet
+
+ // Software Guide : BeginLatex
+ //
+ // Figure~\ref{fig:ROADEXTRACTION_FILTER} shows the result of applying
+ // the road extraction filter to a fusionned Quickbird image.
+ // \begin{figure}
+ // \center
+ // \includegraphics[width=0.44\textwidth]{qb_ExtractRoad_pretty.eps}
+ // \includegraphics[width=0.44\textwidth]{ExtractRoadOutput.eps}
+ // \itkcaption[Road extraction filter application]{Result of applying
+ // the \doxygen{otb}{RoadExtractionFilter} to a fusionned Quickbird
+ // image. From left to right : original image, extracted road with their
+ // likehood values (color are inverted for display).}
+ // \label{fig:ROADEXTRACTION_FILTER}
+ // \end{figure}
+ //
+ // Software Guide : EndLatex
+
+
+ return EXIT_SUCCESS;
+}
+