diff --git a/Examples/Patented/CMakeLists.txt b/Examples/Patented/CMakeLists.txt
index 6fef8431d92517d73343e604b6d8ab8be0fa5e37..b4afe15681ee4a6738f5dba73656c3365900119f 100644
--- a/Examples/Patented/CMakeLists.txt
+++ b/Examples/Patented/CMakeLists.txt
@@ -1,13 +1,6 @@
project(PatentedExamples)
include_regular_expression("^.*$")
-
-#add_executable(FuzzyConnectednessImageFilter FuzzyConnectednessImageFilter.cxx )
-#target_link_libraries(FuzzyConnectednessImageFilter OTBIO OTBCommon )
-
-#add_executable(HybridSegmentationFuzzyVoronoi HybridSegmentationFuzzyVoronoi.cxx )
-#target_link_libraries(HybridSegmentationFuzzyVoronoi OTBIO OTBCommon )
-
#Examples using SIFT
if(OTB_USE_SIFTFAST)
add_executable(SIFTDisparityMapEstimation SIFTDisparityMapEstimation.cxx )
diff --git a/Examples/Patented/FuzzyConnectednessImageFilter.cxx b/Examples/Patented/FuzzyConnectednessImageFilter.cxx
deleted file mode 100644
index 6b9b043eb2ac408b17ec215c8268dee9cece32a9..0000000000000000000000000000000000000000
--- a/Examples/Patented/FuzzyConnectednessImageFilter.cxx
+++ /dev/null
@@ -1,267 +0,0 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
-
-
-// Software Guide : BeginLatex
-//
-// This example illustrates the use of the
-// \doxygen{itk}{SimpleFuzzyConnectednessScalarImageFilter}. This filter computes an
-// affinity map from a seed point provided by the user. This affinity map
-// indicates for every pixels how homogeneous is the path that will link it to
-// the seed point.
-//
-// Please note that the Fuzzy Connectedness algorithm is covered by a Patent
-// \cite{Udupa1998}. For this reason the current example is located in the
-// \texttt{Examples/Patented} subdirectory.
-//
-// In order to use this algorithm we should first include the header files of
-// the filter and the image class.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-#include "itkSimpleFuzzyConnectednessScalarImageFilter.h"
-#include "otbImage.h"
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// Since the FuzzyConnectednessImageFilter requires an estimation of the
-// gray level mean and variance for the region to be segmented, we use here the
-// \doxygen{itk}{ConfidenceConnectedImageFilter} as a preprocessor that produces a
-// rough segmentation and estimates from it the values of the mean and the
-// variance.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-#include "itkConfidenceConnectedImageFilter.h"
-// Software Guide : EndCodeSnippet
-
-#include "otbImageFileReader.h"
-#include "otbImageFileWriter.h"
-
-int main(int argc, char *argv[])
-{
- if (argc < 7)
- {
- std::cerr << "Missing Parameters " << std::endl;
- std::cerr << "Usage: " << argv[0];
- std::cerr << " inputImage outputImage outputAffinityMap " << std::endl;
- std::cerr << " seedX seedY multiplier " << std::endl;
- return 1;
- }
-
- // Software Guide : BeginLatex
- //
- // Next, we declare the pixel type and image dimension and
- // specify the image type to be used as input.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef float InputPixelType;
- const unsigned int Dimension = 2;
- typedef otb::Image<InputPixelType, Dimension> InputImageType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Fuzzy connectedness computes first the affinity map and then thresholds
- // its values in order to get a binary image as output. The type of the
- // binary image is provided as the second template parameter of the filter.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef unsigned char BinaryPixelType;
- typedef otb::Image<BinaryPixelType, Dimension> BinaryImageType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The Confidence connected filter type is instantiated using the input
- // image type and a binary image type for output.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef itk::ConfidenceConnectedImageFilter<
- InputImageType,
- BinaryImageType
- > ConfidenceConnectedFilterType;
-
- ConfidenceConnectedFilterType::Pointer confidenceConnectedFilter =
- ConfidenceConnectedFilterType::New();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The fuzzy segmentation filter type is instantiated here using the input
- // and binary image types as template parameters.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef itk::SimpleFuzzyConnectednessScalarImageFilter<
- InputImageType,
- BinaryImageType
- > FuzzySegmentationFilterType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The fuzzy connectedness segmentation filter is created by invoking the
- // \code{New()} method and assigning the result to a
- // \doxygen{itk}{SmartPointer}.
- //
- // \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!New()}
- // \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!Pointer}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- FuzzySegmentationFilterType::Pointer fuzzysegmenter =
- FuzzySegmentationFilterType::New();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The affinity map can be accessed through the type \code{FuzzySceneType}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef FuzzySegmentationFilterType::FuzzySceneType FuzzySceneType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // We instantiate reader and writer types
- //
- // Software Guide : EndLatex
- typedef otb::ImageFileReader<InputImageType> ReaderType;
- typedef otb::ImageFileWriter<BinaryImageType> WriterType;
- typedef otb::ImageFileWriter<FuzzySceneType> FuzzyWriterType;
-
- ReaderType::Pointer reader = ReaderType::New();
- WriterType::Pointer writer = WriterType::New();
-
- FuzzyWriterType::Pointer fwriter = FuzzyWriterType::New();
-
- reader->SetFileName(argv[1]);
- writer->SetFileName(argv[2]);
- fwriter->SetFileName(argv[3]);
-
- InputImageType::IndexType index;
-
- index[0] = atoi(argv[4]);
- index[1] = atoi(argv[5]);
-
- const double varianceMultiplier = atof(argv[6]);
-
- // Software Guide : BeginLatex
- //
- // The output of the reader is passed as input to the ConfidenceConnected image filter.
- // Then the filter is executed in order to obtain estimations of the mean and variance
- // gray values for the region to be segmented.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- confidenceConnectedFilter->SetInput(reader->GetOutput());
- confidenceConnectedFilter->SetMultiplier(varianceMultiplier);
- confidenceConnectedFilter->SetNumberOfIterations(2);
- confidenceConnectedFilter->AddSeed(index);
-
- confidenceConnectedFilter->Update();
- // Software Guide : EndCodeSnippet
-
- WriterType::Pointer confidenceWriter = WriterType::New();
- confidenceWriter->SetInput(confidenceConnectedFilter->GetOutput());
- confidenceWriter->SetFileName("confidenceConnectedPreprocessing.png");
- confidenceWriter->Update();
-
- // Software Guide : BeginLatex
- //
- // The input that is passed to the fuzzy segmentation filter is taken from
- // the reader.
- //
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetInput()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- fuzzysegmenter->SetInput(reader->GetOutput());
- // Software Guide : EndCodeSnippet
-
- const double meanEstimation = confidenceConnectedFilter->GetMean();
- const double varianceEstimation = confidenceConnectedFilter->GetVariance();
-
- std::cout << "Mean estimation = " << meanEstimation << std::endl;
- std::cout << "Variance estimation = " << varianceEstimation << std::endl;
-
- // Software Guide : BeginLatex
- //
- // The parameters of the fuzzy segmentation filter are defined here. A seed
- // point is provided with the method \code{SetObjectsSeed()} in order to
- // initialize the region to be grown. Estimated values for the mean and
- // variance of the object intensities are also provided with the methods
- // \code{SetMean()} and \code{SetVariance()}, respectively. A threshold
- // value for generating the binary object is preset with the method
- // \code{SetThreshold()}. For details describing the role of the mean and
- // variance on the computation of the segmentation, please see
- // \cite{Udupa1996}.
- //
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetObjectsSeed()}
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetMean()}
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetVariance()}
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetThreshold()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- fuzzysegmenter->SetObjectSeed(index);
- fuzzysegmenter->SetMean(meanEstimation);
- fuzzysegmenter->SetVariance(varianceEstimation);
- fuzzysegmenter->SetThreshold(0.5);
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The execution of the fuzzy segmentation filter is triggered by the
- // \code{Update()} method.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- fuzzysegmenter->Update();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginCodeSnippet
- writer->SetInput(fuzzysegmenter->GetOutput());
- writer->Update();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginCodeSnippet
- fwriter->SetInput(fuzzysegmenter->GetFuzzyScene());
- fwriter->Update();
- // Software Guide : EndCodeSnippet
-
- return EXIT_SUCCESS;
-}
diff --git a/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx b/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
deleted file mode 100644
index 5bcea7bdc8e0a37fafcb3d5af4a7b1f2b88c1ac4..0000000000000000000000000000000000000000
--- a/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
+++ /dev/null
@@ -1,370 +0,0 @@
-/*=========================================================================
-
- 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.
-
-=========================================================================*/
-
-
-// Software Guide : BeginCommandLineArgs
-// INPUTS: {QB_Suburb.png}
-// OUTPUTS: {HybridSegmentationFuzzyVoronoiOutput.png}
-// 111 38 75 20 0.5 3.0
-// Software Guide : EndCommandLineArgs
-
-// Software Guide : BeginLatex
-//
-// This example illustrates the use of the
-// \doxygen{itk}{SimpleFuzzyConnectednessScalarImageFilter} and
-// \doxygen{itk}{VoronoiSegmentationImageFilter} to build a hybrid segmentation that
-// integrates fuzzy connectedness with the Voronoi diagram classification.
-//
-// Please note that the Fuzzy Connectedness algorithm is covered by a Patent
-// \cite{Udupa1998}. For this reason the current example is located in the
-// \texttt{Examples/Patented} subdirectory.
-//
-// First, we include the header files of the two filters.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-#include "itkSimpleFuzzyConnectednessScalarImageFilter.h"
-#include "itkVoronoiSegmentationImageFilter.h"
-// Software Guide : EndCodeSnippet
-
-#include "otbImage.h"
-#include "otbImageFileReader.h"
-#include "otbImageFileWriter.h"
-
-#include "itkRescaleIntensityImageFilter.h"
-
-int main(int argc, char *argv[])
-{
- if (argc < 9)
- {
- std::cerr << "Missing Parameters " << std::endl;
- std::cerr << "Usage: " << argv[0];
- std::cerr << " inputImage outputImage seedX seedY " << std::endl;
- std::cerr <<
- " estimateMean estimateVariance (used by FuzzySegmentation) " << std::endl;
- std::cerr <<
- " meanTolerance standardDeviationTolerance (used by VoronoiSegmentation) "
- << std::endl;
- return 1;
- }
-
- // Software Guide : BeginLatex
- //
- // Next, we declare the pixel type and image dimension and
- // specify the image type to be used as input.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef float InputPixelType;
- const unsigned int Dimension = 2;
- typedef otb::Image<InputPixelType, Dimension> InputImageType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // Fuzzy connectedness segmentation is performed first to generate
- // a rough segmentation that yields a sample from the
- // region to be segmented. A binary result, representing the
- // sample, is used as a prior for the next step. Here, we use the
- // \doxygen{itk}{SimpleFuzzyConnectednessScalarImageFilter}, but we may
- // also utilize any other image segmentation filter instead. The
- // result produced by the fuzzy segmentation filter is stored in a
- // binary image. Below, we declare the type of the image using a
- // pixel type and a spatial dimension.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef unsigned char BinaryPixelType;
- typedef otb::Image<BinaryPixelType, Dimension> BinaryImageType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The fuzzy segmentation filter type is instantiated here using the input
- // and binary image types as template parameters.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef itk::SimpleFuzzyConnectednessScalarImageFilter<
- InputImageType,
- BinaryImageType
- > FuzzySegmentationFilterType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The fuzzy connectedness segmentation filter is created by invoking the
- // \code{New()} method and assigning the result to a
- // \doxygen{itk}{SmartPointer}.
- //
- // \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!New()}
- // \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!Pointer}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- FuzzySegmentationFilterType::Pointer fuzzysegmenter =
- FuzzySegmentationFilterType::New();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // In the next step of the hybrid segmentation method, the prior generated
- // from the fuzzy segmentation is used to build a homogeneity measurement
- // for the object. A VoronoiSegmentationImageFilter uses the
- // homogeneity measurement to drive iterative subdivision of Voronoi regions
- // and to generate the final segmentation result (for details, please see
- // \cite{Imielinska2000b}). In this example, the result of the
- // VoronoiSegmentationImageFilter is sent to a writer. Its output
- // type is conveniently declared as one that is compatible with the writer.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef unsigned char OutputPixelType;
- typedef otb::Image<OutputPixelType, Dimension> OutputImageType;
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The following lines instantiate the Voronoi segmentation filter.
- //
- // \index{itk::Voronoi\-Segmentation\-Image\-Filter!New()}
- // \index{itk::Voronoi\-Segmentation\-Image\-Filter!Pointer}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef itk::VoronoiSegmentationImageFilter<
- InputImageType,
- OutputImageType,
- BinaryImageType>
- VoronoiSegmentationFilterType;
-
- VoronoiSegmentationFilterType::Pointer voronoisegmenter =
- VoronoiSegmentationFilterType::New();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // \begin{figure} \center
- // \includegraphics[width=0.44\textwidth]{QB_Suburb.eps}
- // \includegraphics[width=0.44\textwidth]{HybridSegmentationFuzzyVoronoiOutput.eps}
- // \itkcaption[Segmentation results for the hybrid segmentation
- // approach]{Segmentation results for the hybrid segmentation approach.}
- // \label{fig:HybridSegmentationFuzzyVoronoiOutput}
- // \end{figure}
- //
- // Software Guide : EndLatex
-
- // We instantiate reader and writer types
- //
- typedef otb::ImageFileReader<InputImageType> ReaderType;
- typedef otb::ImageFileWriter<OutputImageType> WriterType;
-
- ReaderType::Pointer reader = ReaderType::New();
- WriterType::Pointer writer = WriterType::New();
-
- reader->SetFileName(argv[1]);
- writer->SetFileName(argv[2]);
-
- // Software Guide : BeginLatex
- //
- // The input that is passed to the fuzzy segmentation filter is taken from
- // the reader.
- //
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetInput()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- fuzzysegmenter->SetInput(reader->GetOutput());
- // Software Guide : EndCodeSnippet
-
- InputImageType::IndexType index;
-
- index[0] = atoi(argv[3]);
- index[1] = atoi(argv[4]);
-
- const float mean = atof(argv[5]);
- const float variance = atof(argv[6]);
-
- const float meanTolerance = atof(argv[7]);
- const float stdTolerance = atof(argv[8]);
-
- // Software Guide : BeginLatex
- //
- // The parameters of the fuzzy segmentation filter are defined here. A seed
- // point is provided with the method \code{SetObjectSeed()} in order to
- // initialize the region to be grown. Estimated values for the mean and
- // variance of the object intensities are also provided with the methods
- // \code{SetMean()} and \code{SetVariance()}, respectively. A threshold
- // value for generating the binary object is preset with the method
- // \code{SetThreshold()}. For details describing the role of the mean and
- // variance on the computation of the segmentation, please see
- // \cite{Udupa1996}.
- //
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetObjectSeed()}
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetMean()}
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetVariance()}
- // \index{itk::Simple\-Fuzzy\-Connectedness\-Scalar\-Image\-Filter!SetThreshold()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- fuzzysegmenter->SetObjectSeed(index);
- fuzzysegmenter->SetMean(mean);
- fuzzysegmenter->SetVariance(variance);
- fuzzysegmenter->SetThreshold(0.5);
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The execution of the fuzzy segmentation filter is triggered by the
- // \code{Update()} method.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- fuzzysegmenter->Update();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The input to the Voronoi diagram classification filter is obtained from
- // the reader and the prior is obtained from the fuzzy segmentation filter.
- //
- // \index{itk::VoronoiSegmentationImageFilter!SetInput()}
- // \index{itk::VoronoiSegmentationImageFilter!TakeAPrior()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- voronoisegmenter->SetInput(reader->GetOutput());
- voronoisegmenter->TakeAPrior(fuzzysegmenter->GetOutput());
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The tolerance levels for testing the mean and standard deviation are set
- // with the methods \code{SetMeanPercentError()} and
- // \code{SetSTDPercentError()}. Note that the
- // fuzzy segmentation filter uses \emph{variance} as parameter while
- // the Voronoi segmentation filter uses the tolerance of the
- // \emph{standard deviation} as a parameter. For more details on how these
- // parameters should be selected, please see \cite{Imielinska2000b}.
- //
- // \index{itk::VoronoiSegmentationImageFilter!SetMeanPercentError()}
- // \index{itk::VoronoiSegmentationImageFilter!SetSTDPercentError()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- voronoisegmenter->SetMeanPercentError(meanTolerance);
- voronoisegmenter->SetSTDPercentError(stdTolerance);
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The \emph{resolution} of the Voronoi diagram classification can be
- // chosen with the method \code{SetMinRegion()}.
- //
- // \index{itk::VoronoiSegmentationImageFilter!SetMinRegion()}
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- voronoisegmenter->SetMinRegion(5);
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The execution of the Voronoi segmentation filter is triggered with the
- // \code{Update()} method.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- voronoisegmenter->Update();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The output of the Voronoi diagram classification is an image mask with
- // zeros everywhere and ones inside the segmented object. This image will
- // appear black on many image viewers since they do not usually stretch
- // the gray levels. Here, we add a \doxygen{itk}{RescaleIntensityImageFilter}
- // in order to expand the dynamic range to more typical values.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- typedef itk::RescaleIntensityImageFilter<OutputImageType, OutputImageType>
- ScalerFilterType;
- ScalerFilterType::Pointer scaler = ScalerFilterType::New();
-
- scaler->SetOutputMinimum(0);
- scaler->SetOutputMaximum(255);
-
- scaler->SetInput(voronoisegmenter->GetOutput());
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // The output of the rescaler is passed to the writer. The invocation
- // of the \code{Update()} method on the writer triggers the execution of
- // the pipeline.
- //
- // Software Guide : EndLatex
-
- // Software Guide : BeginCodeSnippet
- writer->SetInput(scaler->GetOutput());
- writer->Update();
- // Software Guide : EndCodeSnippet
-
- // Software Guide : BeginLatex
- //
- // We execute this program on the image \code{QB\_Suburb.png} available
- // in the directory \code{Examples/Data}. The following parameters are
- // passed to the command line:
- //
- // \small
- // \begin{verbatim}
- //HybridSegmentationFuzzyVoronoi QB_Suburb.png Output.png
- // 111 38 75 20 0.5 2.0
- // \end{verbatim}
- // \normalsize
- //
- // $(111, 38)$ specifies the index position of a seed point in the image,
- // while $75$ and $20$ are the estimated mean and standard deviation,
- // respectively, of the object to be segmented. Finally, $0.5$ and $2.0$
- // are the tolerance for the mean and standard deviation, respectively.
- // Figure~\ref{fig:HybridSegmentationFuzzyVoronoiOutput} shows the input
- // image and the binary mask resulting from the segmentation.
- //
- // Note that in order to successfully segment other images, these
- // parameters have to be adjusted to reflect the data.
- //
- // Software Guide : EndLatex
-
- return EXIT_SUCCESS;
-}