From ad83dd5aa0d32451abbf1cdcbe1412c31ce2d1d9 Mon Sep 17 00:00:00 2001
From: Jordi Inglada <jordi.inglada@orfeo-toolbox.org>
Date: Mon, 3 Jul 2006 12:41:10 +0000
Subject: [PATCH] Corrections namespace pour doxygen
---
Examples/BasicFilters/LeeImageFilter.cxx | 12 ++---
.../ChangeDetectionFrameworkExample.cxx | 10 ++--
Examples/ChangeDetection/CorrelChDet.cxx | 6 +--
Examples/ChangeDetection/DiffChDet.cxx | 8 +--
Examples/ChangeDetection/RatioChDet.cxx | 6 +--
.../BayesianPluginClassifier.cxx | 16 +++---
...ationMaximizationMixtureModelEstimator.cxx | 10 ++--
.../KdTreeBasedKMeansClustering.cxx | 24 ++++-----
.../ScalarImageKmeansClassifier.cxx | 10 ++--
.../ScalarImageKmeansModelEstimator.cxx | 2 +-
.../ScalarImageMarkovRandomField1.cxx | 18 +++----
.../Containers/TreeContainer.cxx | 20 +++----
Examples/DataRepresentation/Image/Image1.cxx | 8 +--
Examples/DataRepresentation/Image/Image2.cxx | 6 +--
Examples/DataRepresentation/Image/Image3.cxx | 2 +-
Examples/DataRepresentation/Image/Image4.cxx | 8 +--
Examples/DataRepresentation/Image/Image5.cxx | 6 +--
.../DataRepresentation/Image/RGBImage.cxx | 8 +--
.../DataRepresentation/Image/VectorImage.cxx | 10 ++--
Examples/DataRepresentation/Mesh/Mesh1.cxx | 6 +--
Examples/DataRepresentation/Mesh/Mesh2.cxx | 10 ++--
Examples/DataRepresentation/Mesh/Mesh3.cxx | 4 +-
.../DataRepresentation/Mesh/PointSet2.cxx | 10 ++--
.../DataRepresentation/Mesh/PointSet3.cxx | 6 +--
.../Mesh/PointSetWithVectors.cxx | 10 ++--
.../Path/PolyLineParametricPath1.cxx | 2 +-
.../FeatureExtraction/AlignmentsExample.cxx | 16 +++---
...AssymmetricFusionOfLineDetectorExample.cxx | 10 ++--
.../ComplexMomentImageExample.cxx | 4 +-
.../ComplexMomentPathExample.cxx | 8 +--
.../CorrelationLineDetectorExample.cxx | 12 ++---
.../ExtractSegmentsByStepsExample.cxx | 12 ++---
.../ExtractSegmentsExample.cxx | 8 +--
.../FlusserMomentImageExample.cxx | 6 +--
Examples/FeatureExtraction/HarrisExample.cxx | 20 +++----
.../HuMomentImageExample.cxx | 6 +--
.../FeatureExtraction/LocalHoughExample.cxx | 12 ++---
.../RatioLineDetectorExample.cxx | 12 ++---
.../TouziEdgeDetectorExample.cxx | 6 +--
.../Filtering/BinaryThresholdImageFilter.cxx | 10 ++--
.../CannyEdgeDetectionImageFilter.cxx | 6 +--
Examples/Filtering/ThresholdImageFilter.cxx | 10 ++--
Examples/IO/ExtractROI.cxx | 24 ++++-----
Examples/IO/ImageReadCastWrite.cxx | 2 +-
.../IO/ImageReadRegionOfInterestWrite.cxx | 12 ++---
Examples/IO/ImageReadWrite.cxx | 10 ++--
Examples/IO/MetadataExample.cxx | 6 +--
Examples/IO/MultibandImageReadWrite.cxx | 8 +--
Examples/IO/RGBImageReadWrite.cxx | 4 +-
Examples/IO/StreamingImageReadWrite.cxx | 10 ++--
Examples/Installation/HelloWorld.cxx | 4 +-
.../SVMImageClassificationExample.cxx | 12 ++---
...ageEstimatorClassificationMultiExample.cxx | 6 +--
.../SVMImageModelEstimatorExample.cxx | 4 +-
.../SVMPointSetClassificationExample.cxx | 10 ++--
.../SVMPointSetModelEstimatorExample.cxx | 4 +-
.../FuzzyConnectednessImageFilter.cxx | 6 +--
.../HybridSegmentationFuzzyVoronoi.cxx | 53 ++++---------------
Examples/Segmentation/ConfidenceConnected.cxx | 12 ++---
.../ConnectedThresholdImageFilter.cxx | 10 ++--
.../Segmentation/FastMarchingImageFilter.cxx | 32 +++++------
.../IsolatedConnectedImageFilter.cxx | 8 +--
.../NeighborhoodConnectedImageFilter.cxx | 10 ++--
.../OtsuMultipleThresholdImageFilter.cxx | 4 +-
.../Segmentation/OtsuThresholdImageFilter.cxx | 10 ++--
.../Segmentation/WatershedSegmentation.cxx | 18 +++----
Examples/Visu/GreyVisuExample.cxx | 4 +-
Examples/Visu/VisuExample1.cxx | 4 +-
68 files changed, 326 insertions(+), 357 deletions(-)
diff --git a/Examples/BasicFilters/LeeImageFilter.cxx b/Examples/BasicFilters/LeeImageFilter.cxx
index 1b213c1103..4a43549434 100644
--- a/Examples/BasicFilters/LeeImageFilter.cxx
+++ b/Examples/BasicFilters/LeeImageFilter.cxx
@@ -31,7 +31,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::LeeImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{LeeImageFilter}.
// This filter belongs to the family of the edge-preserving smoothing
// filters which are usually used for speckle reduction in radar
// images. The Lee filter \cite{LeeFilter} aplies a linear regression
@@ -96,7 +96,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -107,7 +107,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -138,7 +138,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::LeeImageFilter}.
+ // \doxygen{otb}{LeeImageFilter}.
//
// \index{otb::LeeImageFilter!SetInput()}
// \index{otb::FileImageReader!GetOutput()}
@@ -199,13 +199,13 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.44\textwidth]{GomaSmall.eps}
// \includegraphics[width=0.44\textwidth]{GomaSmallLeeFiltered.eps}
// \itkcaption[Lee Filter Application]{Result of applying the
- // \doxygen{otb::LeeImageFilter} to a SAR image.}
+ // \doxygen{otb}{LeeImageFilter} to a SAR image.}
// \label{fig:LEE_FILTER}
// \end{figure}
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::FrostImageFilter}
+ // \item \doxygen{otb}{FrostImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx b/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
index 1b2de12149..a79aa883db 100644
--- a/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
+++ b/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
@@ -29,7 +29,7 @@
// This example illustrates the Change Detector framework implemented
// in OTB. This framework uses the generic programming approach. All
// change detection filters are
-// \doxygen{otb::BinaryFunctorNeighborhoodImageFilter}s, that is, they
+// \doxygen{otb}{BinaryFunctorNeighborhoodImageFilter}s, that is, they
// are filters taking two images as input and providing one image as
// output. The change detection computation itself is performed on a
// the neighborhood of each pixel of the input images.
@@ -62,7 +62,7 @@
//
// Since the change detectors operate on neighborhoods, the functor
// call will take 2 arguments which are
-// \doxygen{itk::ConstNeighborhoodIterator}s.
+// \doxygen{itk}{ConstNeighborhoodIterator}s.
//
// The change detector functor is templated over the types of the
// input iterators and the output result type. The core of the change
@@ -106,7 +106,7 @@ public:
//
// The next step is the definition of the change detector filter. As
// stated above, this filter will inherit from
-// \doxygen{otb::BinaryFunctorNeighborhoodImageFilter} which is
+// \doxygen{otb}{BinaryFunctorNeighborhoodImageFilter} which is
// templated over the 2 input image types, the output image type and
// the functor used to perform the change detection operation.
//
@@ -157,7 +157,7 @@ private:
//
// Pay attention to the fact that no \code{.txx} file is needed, since
// filtering operation is implemented in the
-// \doxygen{otb::BinaryFunctorNeighborhoodImageFilter} class. So all
+// \doxygen{otb}{BinaryFunctorNeighborhoodImageFilter} class. So all
// the algorithmics part is inside the functor.
//
// We can now write a program using the change detector.
@@ -198,7 +198,7 @@ int main(int argc, char* argv[] )
// Software Guide : BeginLatex
//
// We declare the readers, the writer, but also the
- // \doxygen{itk::RescaleIntensityImageFilter} which will be used to
+ // \doxygen{itk}{RescaleIntensityImageFilter} which will be used to
// rescale the result before writing it to a file.
//
// SoftwareGuide : EndLatex
diff --git a/Examples/ChangeDetection/CorrelChDet.cxx b/Examples/ChangeDetection/CorrelChDet.cxx
index d1ae71059e..ccea83417d 100644
--- a/Examples/ChangeDetection/CorrelChDet.cxx
+++ b/Examples/ChangeDetection/CorrelChDet.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
// This example illustrates the class
-// \doxygen{otb::CorrelationChangeDetector} for detecting changes
+// \doxygen{otb}{CorrelationChangeDetector} for detecting changes
// between pairs of images. This filter computes the correlation coefficient in
// the neighborhood of each pixel of the pair of images to be compared. This
// example will use the images shown in
@@ -88,7 +88,7 @@ int main(int argc, char* argv[] )
// can be vey large, we will force the pipeline to use
// streaming. For this purpose, the file writer will be
// streamed. This is achieved by using the
- // \doxygen{otb::StreamingImageFileWriter} class.
+ // \doxygen{otb}{StreamingImageFileWriter} class.
//
// Software Guide : EndLatex
@@ -115,7 +115,7 @@ int main(int argc, char* argv[] )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::CorrelationChangeDetector} is templated over
+ // The \doxygen{otb}{CorrelationChangeDetector} is templated over
// the types of the two input images and the type of the generated change
// image.
//
diff --git a/Examples/ChangeDetection/DiffChDet.cxx b/Examples/ChangeDetection/DiffChDet.cxx
index 9de75a40ef..18c191d0fd 100755
--- a/Examples/ChangeDetection/DiffChDet.cxx
+++ b/Examples/ChangeDetection/DiffChDet.cxx
@@ -35,7 +35,7 @@
// Software Guide : BeginLatex
// This example illustrates the class
-// \doxygen{otb::MeanDifferenceImageFilter} for detecting changes
+// \doxygen{otb}{MeanDifferenceImageFilter} for detecting changes
// between pairs of images. This filter computes the mean intensity in
// the neighborhood of each pixel of the pair of images to be compared
// and uses the difference of means as a change indicator. This
@@ -104,7 +104,7 @@ int main(int argc, char* argv[] )
// can be vey large, we will force the pipeline to use
// streaming. For this purpose, the file writer will be
// streamed. This is achieved by using the
- // \doxygen{otb::StreamingImageFileWriter} class.
+ // \doxygen{otb}{StreamingImageFileWriter} class.
//
// Software Guide : EndLatex
@@ -119,7 +119,7 @@ int main(int argc, char* argv[] )
// The change detector will give positive and negative values
// depending on the sign of the difference. We are usually
// interested only in the asbolute value of the difference. For
- // this purpose, we will use the \doxygen{itk::AbsImageFilter}. Also, before
+ // this purpose, we will use the \doxygen{itk}{AbsImageFilter}. Also, before
// saving the image to a file in, for instance, PNG format, we will
// rescale the results of the change detection in order to use all
// the output pixel type range of values.
@@ -136,7 +136,7 @@ int main(int argc, char* argv[] )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::MeanDifferenceImageFilter} is templated over
+ // The \doxygen{otb}{MeanDifferenceImageFilter} is templated over
// the types of the two input images and the type of the generated change
// image.
//
diff --git a/Examples/ChangeDetection/RatioChDet.cxx b/Examples/ChangeDetection/RatioChDet.cxx
index f079a46f9b..014910d7b0 100644
--- a/Examples/ChangeDetection/RatioChDet.cxx
+++ b/Examples/ChangeDetection/RatioChDet.cxx
@@ -26,7 +26,7 @@
// Software Guide : BeginLatex
// This example illustrates the class
-// \doxygen{otb::MeanRatioImageFilter} for detecting changes
+// \doxygen{otb}{MeanRatioImageFilter} for detecting changes
// between pairs of images. This filter computes the mean intensity in
// the neighborhood of each pixel of the pair of images to be compared
// and uses the ratio of means as a change indicator. This change
@@ -96,7 +96,7 @@ int main(int argc, char* argv[] )
// can be vey large, we will force the pipeline to use
// streaming. For this purpose, the file writer will be
// streamed. This is achieved by using the
- // \doxygen{otb::StreamingImageFileWriter} class.
+ // \doxygen{otb}{StreamingImageFileWriter} class.
//
// Software Guide : EndLatex
@@ -126,7 +126,7 @@ int main(int argc, char* argv[] )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::MeanRatioImageFilter} is templated over
+ // The \doxygen{otb}{MeanRatioImageFilter} is templated over
// the types of the two input images and the type of the generated change
// image.
//
diff --git a/Examples/Classification/BayesianPluginClassifier.cxx b/Examples/Classification/BayesianPluginClassifier.cxx
index de878d0c3e..dfea1f4286 100644
--- a/Examples/Classification/BayesianPluginClassifier.cxx
+++ b/Examples/Classification/BayesianPluginClassifier.cxx
@@ -30,12 +30,12 @@
// all the components of the classifier system and the data flow. This system
// differs with the previous k-means clustering algorithms in several
// ways. The biggest difference is that this classifier uses the
-// \subdoxygen{itk::Statistics}{GaussianDensityFunction}s as membership functions
-// instead of the \subdoxygen{itk::Statistics}{EuclideanDistance}. Since the
+// \subdoxygen{itk}{Statistics}{GaussianDensityFunction}s as membership functions
+// instead of the \subdoxygen{itk}{Statistics}{EuclideanDistance}. Since the
// membership function is different, the membership function requires a
// different set of parameters, mean vectors and covariance matrices. We
-// choose the \subdoxygen{itk::Statistics}{MeanCalculator} (sample mean) and the
-// \subdoxygen{itk::Statistics}{CovarianceCalculator} (sample covariance) for the
+// choose the \subdoxygen{itk}{Statistics}{MeanCalculator} (sample mean) and the
+// \subdoxygen{itk}{Statistics}{CovarianceCalculator} (sample covariance) for the
// estimation algorithms of the two parameters. If we want more robust
// estimation algorithm, we can replace these estimation algorithms with more
// alternatives without changing other components in the classifier system.
@@ -52,10 +52,10 @@
// \protect\label{fig:BayesianPluginClassifier}
// \end{figure}
//
-// We use the \subdoxygen{itk::Statistics}{ListSample} as the sample (test
-// and training). The \doxygen{itk::Vector} is our measurement vector
+// We use the \subdoxygen{itk}{Statistics}{ListSample} as the sample (test
+// and training). The \doxygen{itk}{Vector} is our measurement vector
// class. To store measurement vectors into two separate sample
-// containers, we use the \subdoxygen{itk::Statistics}{Subsample} objects.
+// containers, we use the \subdoxygen{itk}{Statistics}{Subsample} objects.
//
// Software Guide : EndLatex
@@ -93,7 +93,7 @@
// Software Guide : BeginLatex
//
// We will fill the sample with random variables from two normal
-// distribution using the \subdoxygen{itk::Statistics}{NormalVariateGenerator}.
+// distribution using the \subdoxygen{itk}{Statistics}{NormalVariateGenerator}.
//
// Software Guide : EndLatex
diff --git a/Examples/Classification/ExpectationMaximizationMixtureModelEstimator.cxx b/Examples/Classification/ExpectationMaximizationMixtureModelEstimator.cxx
index 603d25e769..c7f053d346 100644
--- a/Examples/Classification/ExpectationMaximizationMixtureModelEstimator.cxx
+++ b/Examples/Classification/ExpectationMaximizationMixtureModelEstimator.cxx
@@ -68,13 +68,13 @@
// distributions belonging to exponential family such as Poisson,
// Binomial, Exponential, and Normal distributions have analytical
// solutions for updating the parameter set. The
-// \subdoxygen{itk::Statistics}{ExpectationMaximizationMixtureModelEstimator}
+// \subdoxygen{itk}{Statistics}{ExpectationMaximizationMixtureModelEstimator}
// class assumes that such type of components.
//
-// In the following example we use the \subdoxygen{itk::Statistics}{ListSample} as
-// the sample (test and training). The \subdoxygen{itk::Vector} is our measurement
+// In the following example we use the \subdoxygen{itk}{Statistics}{ListSample} as
+// the sample (test and training). The \subdoxygen{itk}{Vector} is our measurement
// vector class. To store measurement vectors into two separate sample
-// container, we use the \subdoxygen{itk::Statistics}{Subsample} objects.
+// container, we use the \subdoxygen{itk}{Statistics}{Subsample} objects.
//
// Software Guide : EndLatex
@@ -97,7 +97,7 @@
// Software Guide : BeginLatex
//
// We will fill the sample with random variables from two normal
-// distribution using the \subdoxygen{itk::Statistics}{NormalVariateGenerator}.
+// distribution using the \subdoxygen{itk}{Statistics}{NormalVariateGenerator}.
//
// Software Guide : EndLatex
diff --git a/Examples/Classification/KdTreeBasedKMeansClustering.cxx b/Examples/Classification/KdTreeBasedKMeansClustering.cxx
index f1ded1b216..6da0dffd1e 100644
--- a/Examples/Classification/KdTreeBasedKMeansClustering.cxx
+++ b/Examples/Classification/KdTreeBasedKMeansClustering.cxx
@@ -42,13 +42,13 @@
// measurement vector that changes its cluster membership from the
// previous iteration, then the algorithm stops.
//
-// The \subdoxygen{itk::Statistics}{KdTreeBasedKmeansEstimator} is a variation of
+// The \subdoxygen{itk}{Statistics}{KdTreeBasedKmeansEstimator} is a variation of
// this logic. The k-means clustering algorithm is computationally very
// expensive because it has to recalculate the mean at each iteration. To
// update the mean values, we have to calculate the distance between k means
// and each and every measurement vector. To reduce the computational burden,
// the KdTreeBasedKmeansEstimator uses a special data structure: the
-// k-d tree (\subdoxygen{itk::Statistics}{KdTree}) with additional
+// k-d tree (\subdoxygen{itk}{Statistics}{KdTree}) with additional
// information. The additional information includes the number and the vector
// sum of measurement vectors under each node under the tree architecture.
//
@@ -61,8 +61,8 @@
// \cite{Kanungo2000}. Our implementation of this scheme follows the
// article by the Kanungo et al \cite{Kanungo2000}.
//
-// We use the \subdoxygen{itk::Statistics}{ListSample} as the input sample, the
-// \doxygen{itk::Vector} as the measurement vector. The following code
+// We use the \subdoxygen{itk}{Statistics}{ListSample} as the input sample, the
+// \doxygen{itk}{Vector} as the measurement vector. The following code
// snippet includes their header files.
//
// Software Guide : EndLatex
@@ -74,12 +74,12 @@
// Software Guide : BeginLatex
//
-// Since this k-means algorithm requires a \subdoxygen{itk::Statistics}{KdTree}
+// Since this k-means algorithm requires a \subdoxygen{itk}{Statistics}{KdTree}
// object as an input, we include the KdTree class header file. As mentioned
// above, we need a k-d tree with the vector sum and the number of
// measurement vectors. Therefore we use the
-// \subdoxygen{itk::Statistics}{WeightedCentroidKdTreeGenerator} instead of the
-// \subdoxygen{itk::Statistics}{KdTreeGenerator} that generate a k-d tree without
+// \subdoxygen{itk}{Statistics}{WeightedCentroidKdTreeGenerator} instead of the
+// \subdoxygen{itk}{Statistics}{KdTreeGenerator} that generate a k-d tree without
// such additional information.
//
// Software Guide : EndLatex
@@ -104,10 +104,10 @@
// Software Guide : BeginLatex
//
// To generate the clusters, we must create k instances of
-// \subdoxygen{itk::Statistics}{EuclideanDistance} function as the membership
+// \subdoxygen{itk}{Statistics}{EuclideanDistance} function as the membership
// functions for each cluster and plug that---along with a sample---into an
-// \subdoxygen{itk::Statistics}{SampleClassifier} object to get a
-// \subdoxygen{itk::Statistics}{MembershipSample} that stores pairs of measurement
+// \subdoxygen{itk}{Statistics}{SampleClassifier} object to get a
+// \subdoxygen{itk}{Statistics}{MembershipSample} that stores pairs of measurement
// vectors and their associated class labels (k labels).
//
// Software Guide : EndLatex
@@ -121,7 +121,7 @@
// Software Guide : BeginLatex
//
// We will fill the sample with random variables from two normal
-// distribution using the \subdoxygen{itk::Statistics}{NormalVariateGenerator}.
+// distribution using the \subdoxygen{itk}{Statistics}{NormalVariateGenerator}.
//
// Software Guide : EndLatex
@@ -281,7 +281,7 @@ int main()
// the estimated k means and the measurement vectors. We use the
// EuclideanDistance class as membership functions. Our choice
// for the decision rule is the
- // \subdoxygen{itk::Statistics}{MinimumDecisionRule} that returns the
+ // \subdoxygen{itk}{Statistics}{MinimumDecisionRule} that returns the
// index of the membership functions that have the smallest value for
// a measurement vector.
//
diff --git a/Examples/Classification/ScalarImageKmeansClassifier.cxx b/Examples/Classification/ScalarImageKmeansClassifier.cxx
index 79a1a4d9c0..f5f84e5c90 100755
--- a/Examples/Classification/ScalarImageKmeansClassifier.cxx
+++ b/Examples/Classification/ScalarImageKmeansClassifier.cxx
@@ -30,7 +30,7 @@
// This example shows how to use the KMeans model for classifying the pixel of
// a scalar image.
//
-// The \subdoxygen{itk::Statistics}{ScalarImageKmeansImageFilter} is used for taking
+// The \subdoxygen{itk}{Statistics}{ScalarImageKmeansImageFilter} is used for taking
// a scalar image and applying the K-Means algorithm in order to define classes
// that represents statistical distributions of intensity values in the pixels.
// The classes are then used in this filter for generating a labeled image where
@@ -65,7 +65,7 @@ int main( int argc, char * argv [] )
//
// First we define the pixel type and dimension of the image that we intend to
// classify. With this image type we can also declare the
-// \doxygen{otb::ImageFileReader} needed for reading the input image, create one and
+// \doxygen{otb}{ImageFileReader} needed for reading the input image, create one and
// set its input filename.
//
// Software Guide : EndLatex
@@ -87,7 +87,7 @@ int main( int argc, char * argv [] )
// Software Guide : BeginLatex
//
// With the \code{ImageType} we instantiate the type of the
-// \doxygen{itk::ScalarImageKmeansImageFilter} that will compute the K-Means model
+// \doxygen{itk}{ScalarImageKmeansImageFilter} that will compute the K-Means model
// and then classify the image pixels.
//
// Software Guide : EndLatex
@@ -166,11 +166,11 @@ int main( int argc, char * argv [] )
// Software Guide : BeginLatex
//
-// The \doxygen{itk::ScalarImageKmeansImageFilter} is predefined for producing an 8
+// The \doxygen{itk}{ScalarImageKmeansImageFilter} is predefined for producing an 8
// bits scalar image as output. This output image contains labels associated
// to each one of the classes in the K-Means algorithm. In the following lines
// we use the \code{OutputImageType} in order to instantiate the type of a
-// \doxygen{otb::ImageFileWriter}. Then create one, and connect it to the output of
+// \doxygen{otb}{ImageFileWriter}. Then create one, and connect it to the output of
// the classification filter.
//
// Software Guide : EndLatex
diff --git a/Examples/Classification/ScalarImageKmeansModelEstimator.cxx b/Examples/Classification/ScalarImageKmeansModelEstimator.cxx
index f0110bd5a6..456e521da4 100755
--- a/Examples/Classification/ScalarImageKmeansModelEstimator.cxx
+++ b/Examples/Classification/ScalarImageKmeansModelEstimator.cxx
@@ -26,7 +26,7 @@
//
// This example shows how to compute the KMeans model of an Scalar Image.
//
-// The \subdoxygen{itk::Statistics}{KdTreeBasedKmeansEstimator} is used for taking
+// The \subdoxygen{itk}{Statistics}{KdTreeBasedKmeansEstimator} is used for taking
// a scalar image and applying the K-Means algorithm in order to define classes
// that represents statistical distributions of intensity values in the pixels.
// One of the drawbacks of this technique is that the spatial
diff --git a/Examples/Classification/ScalarImageMarkovRandomField1.cxx b/Examples/Classification/ScalarImageMarkovRandomField1.cxx
index ad246c39ae..62e65e72b3 100755
--- a/Examples/Classification/ScalarImageMarkovRandomField1.cxx
+++ b/Examples/Classification/ScalarImageMarkovRandomField1.cxx
@@ -31,7 +31,7 @@
// This example shows how to use the Markov Random Field approach for
// classifying the pixel of a scalar image.
//
-// The \subdoxygen{itk::Statistics}{MRFImageFilter} is used for refining an initial
+// The \subdoxygen{itk}{Statistics}{MRFImageFilter} is used for refining an initial
// classification by introducing the spatial coherence of the labels. The user
// should provide two images as input. The first image is the one to be
// classified while the second image is an image of labels representing an
@@ -110,7 +110,7 @@ int main( int argc, char * argv [] )
//
// First we define the pixel type and dimension of the image that we intend to
// classify. With this image type we can also declare the
-// \doxygen{otb::ImageFileReader} needed for reading the input image, create one and
+// \doxygen{otb}{ImageFileReader} needed for reading the input image, create one and
// set its input filename.
//
// Software Guide : EndLatex
@@ -155,7 +155,7 @@ int main( int argc, char * argv [] )
// whose pixels have multiple components, that is, images of vector type, we
// must adapt our scalar image in order to satisfy the interface expected by
// the \code{MRFImageFilter}. We do this by using the
-// \doxygen{itk::ScalarToArrayCastImageFilter}. With this filter we will present our
+// \doxygen{itk}{ScalarToArrayCastImageFilter}. With this filter we will present our
// scalar image as a vector image whose vector pixels contain a single
// component.
//
@@ -179,7 +179,7 @@ int main( int argc, char * argv [] )
//
// With the input image type \code{ImageType} and labeled image type
// \code{LabelImageType} we instantiate the type of the
-// \doxygen{itk::MRFImageFilter} that will apply the Markov Random Field algorithm
+// \doxygen{itk}{MRFImageFilter} that will apply the Markov Random Field algorithm
// in order to refine the pixel classification.
//
// Software Guide : EndLatex
@@ -234,7 +234,7 @@ int main( int argc, char * argv [] )
// Given that the MRF filter needs to continually relabel the pixels, it needs
// access to a set of membership functions that will measure to what degree
// every pixel belongs to a particular class. The classification is performed
-// by the \doxygen{itk::ImageClassifierBase} class, that is instantiated using the
+// by the \doxygen{itk}{ImageClassifierBase} class, that is instantiated using the
// type of the input vector image and the type of the labeled image.
//
// Software Guide : EndLatex
@@ -273,7 +273,7 @@ int main( int argc, char * argv [] )
// Software Guide : BeginLatex
//
// We now instantiate the membership functions. In this case we use the
-// \subdoxygen{itk::Statistics}{DistanceToCentroidMembershipFunction} class
+// \subdoxygen{itk}{Statistics}{DistanceToCentroidMembershipFunction} class
// templated over the pixel type of the vector image, which in our example
// happens to be a vector of dimension 1.
//
@@ -369,7 +369,7 @@ int main( int argc, char * argv [] )
// functions have comparable value. This is necessary since the label
// image and the input image can have different dynamic ranges. The fidelity
// function is usually computed using a distance function, such as the
-// \doxygen{itk::DistanceToCentroidMembershipFunction} or one of the other
+// \doxygen{itk}{DistanceToCentroidMembershipFunction} or one of the other
// membership functions. They tend to have values in the order of the means
// specified.
// Software Guide : EndLatex
@@ -408,10 +408,10 @@ int main( int argc, char * argv [] )
// Software Guide : BeginLatex
//
-// The output image produced by the \doxygen{itk::MRFImageFilter} has the same pixel
+// The output image produced by the \doxygen{itk}{MRFImageFilter} has the same pixel
// type as the labeled input image. In the following lines we use the
// \code{OutputImageType} in order to instantiate the type of a
-// \doxygen{otb::ImageFileWriter}. Then create one, and connect it to the output of
+// \doxygen{otb}{ImageFileWriter}. Then create one, and connect it to the output of
// the classification filter after passing it through an intensity rescaler
// to rescale it to an 8 bit dynamic range
//
diff --git a/Examples/DataRepresentation/Containers/TreeContainer.cxx b/Examples/DataRepresentation/Containers/TreeContainer.cxx
index 1302e0b838..ca5091395e 100644
--- a/Examples/DataRepresentation/Containers/TreeContainer.cxx
+++ b/Examples/DataRepresentation/Containers/TreeContainer.cxx
@@ -27,9 +27,9 @@
//
// \index{itk::TreeContainer}
//
-// This example shows how to use the \doxygen{TreeContainer} and the
+// This example shows how to use the \doxygen{itk}{TreeContainer} and the
// associated TreeIterators.
-// The \doxygen{TreeContainer} implements the notion of tree and is
+// The \doxygen{itk}{TreeContainer} implements the notion of tree and is
// templated over the type of node so it can virtually handle any
// objects. Each node is supposed to have only one parent so no cycle
// is present in the tree. No checking is done to ensure a cycle-free
@@ -86,7 +86,7 @@ int main(int, char* [])
tree->Add(7,1);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// We define an \doxygen{LevelOrderTreeIterator} to parse the tree in level order.
+// We define an \doxygen{itk}{LevelOrderTreeIterator} to parse the tree in level order.
// This particular iterator takes three arguments. The first one is the actual tree
// to be parsed, the second one is the maximum depth level and the third one is the
// starting node. The \code{GetNode()} function return a node given its value. Once
@@ -118,7 +118,7 @@ int main(int, char* [])
levelIt.CountChildren();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{ChildTreeIterator} provides another way to iterate through a tree
+// The \doxygen{itk}{ChildTreeIterator} provides another way to iterate through a tree
// by listing all the children of a node.
// Software Guide : EndLatex
std::cout << "ChildTreeIterator:" << std::endl;
@@ -156,7 +156,7 @@ int main(int, char* [])
delete childItClone;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{LeafTreeIterator} iterates through the leaves of the tree.
+// The \doxygen{itk}{LeafTreeIterator} iterates through the leaves of the tree.
// Software Guide : EndLatex
std::cout << "LeafTreeIterator:" << std::endl;
// Software Guide : BeginCodeSnippet
@@ -170,7 +170,7 @@ int main(int, char* [])
std::cout << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{InOrderTreeIterator} iterates through the tree
+// The \doxygen{itk}{InOrderTreeIterator} iterates through the tree
// in the order from left to right.
// Software Guide : EndLatex
std::cout << "InOrderTreeIterator:" << std::endl;
@@ -185,7 +185,7 @@ int main(int, char* [])
std::cout << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{PreOrderTreeIterator} iterates through the tree
+// The \doxygen{itk}{PreOrderTreeIterator} iterates through the tree
// from left to right but do a depth first search.
// Software Guide : EndLatex
std::cout << "PreOrderTreeIterator:" << std::endl;
@@ -200,7 +200,7 @@ int main(int, char* [])
std::cout << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{PostOrderTreeIterator} iterates through the tree
+// The \doxygen{itk}{PostOrderTreeIterator} iterates through the tree
// from left to right but goes from the leaves to the root in the search.
// Software Guide : EndLatex
std::cout << "PostOrderTreeIterator:" << std::endl;
@@ -215,7 +215,7 @@ int main(int, char* [])
std::cout << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{RootTreeIterator} goes from one node to the
+// The \doxygen{itk}{RootTreeIterator} goes from one node to the
// root. The second arguments is the starting node. Here we go from the leaf
// node (value = 6) up to the root.
// Software Guide : EndLatex
@@ -253,7 +253,7 @@ int main(int, char* [])
PreOrderIt2.Add(5);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
-// The \doxygen{TreeIteratorClone} can be used to have a generic copy of
+// The \doxygen{itk}{TreeIteratorClone} can be used to have a generic copy of
// an iterator.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
diff --git a/Examples/DataRepresentation/Image/Image1.cxx b/Examples/DataRepresentation/Image/Image1.cxx
index 5d3307313d..884c6b9b56 100644
--- a/Examples/DataRepresentation/Image/Image1.cxx
+++ b/Examples/DataRepresentation/Image/Image1.cxx
@@ -24,7 +24,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates how to manually construct an \doxygen{otb::Image}
+// This example illustrates how to manually construct an \doxygen{otb}{Image}
// class. The following is the minimal code needed to instantiate, declare
// and create the image class.
//
@@ -63,7 +63,7 @@ int main(int, char *[])
//
// The image can then be created by invoking the \code{New()} operator
// from the corresponding image type and assigning the result
- // to a \doxygen{itk::SmartPointer}.
+ // to a \doxygen{itk}{SmartPointer}.
//
// \index{Image!Pointer}
// \index{Image!New()}
@@ -92,8 +92,8 @@ int main(int, char *[])
// instantiated as previously shown, and that regions describing the image are
// then associated with it.
//
- // A region is defined by two classes: the \doxygen{itk::Index} and
- // \doxygen{itk::Size} classes. The origin of the region within the
+ // A region is defined by two classes: the \doxygen{itk}{Index} and
+ // \doxygen{itk}{Size} classes. The origin of the region within the
// image with which it is associated is defined by Index. The
// extent, or size, of the region is defined by Size. Index
// is represented by a n-dimensional array where each component is an
diff --git a/Examples/DataRepresentation/Image/Image2.cxx b/Examples/DataRepresentation/Image/Image2.cxx
index a9f95e68e9..a7d6544650 100644
--- a/Examples/DataRepresentation/Image/Image2.cxx
+++ b/Examples/DataRepresentation/Image/Image2.cxx
@@ -27,7 +27,7 @@
// Software Guide : BeginLatex
//
// The first thing required to read an image from a file is to include
-// the header file of the \doxygen{otb::ImageFileReader} class.
+// the header file of the \doxygen{otb}{ImageFileReader} class.
//
// Software Guide : EndLatex
@@ -78,7 +78,7 @@ int main( int , char * argv[])
// Software Guide : BeginLatex
//
// The reader type can now be used to create one reader object. A
- // \doxygen{itk::SmartPointer} (defined by the \code{::Pointer}
+ // \doxygen{itk}{SmartPointer} (defined by the \code{::Pointer}
// notation) is used to receive the reference to the newly created
// reader. The \code{New()}
// method is invoked to create an instance of the image reader.
@@ -99,7 +99,7 @@ int main( int , char * argv[])
// of the image to be loaded in memory. This is provided through
// the \code{SetFileName()} method. The file format here is inferred
// from the filename extension. The user may also explicitly specify the
- // data format explicitly using the \doxygen{itk::ImageIO} (See
+ // data format explicitly using the \doxygen{itk}{ImageIO} (See
// Chapter~\ref{sec:ImagReadWrite} \pageref{sec:ImagReadWrite} for more
// information):
//
diff --git a/Examples/DataRepresentation/Image/Image3.cxx b/Examples/DataRepresentation/Image/Image3.cxx
index e375cd1c16..afdb73fa55 100644
--- a/Examples/DataRepresentation/Image/Image3.cxx
+++ b/Examples/DataRepresentation/Image/Image3.cxx
@@ -76,7 +76,7 @@ int main(int, char *[])
// unique index. An index is an array of integers that defines the position
// of the pixel along each coordinate dimension of the image. The IndexType
// is automatically defined by the image and can be accessed using the
- // scope operator like \doxygen{itk::Index}. The length of the array will match
+ // scope operator like \doxygen{itk}{Index}. The length of the array will match
// the dimensions of the associated image.
//
// The following code illustrates the declaration of an index variable and
diff --git a/Examples/DataRepresentation/Image/Image4.cxx b/Examples/DataRepresentation/Image/Image4.cxx
index fbdb8e031b..615b3769fe 100644
--- a/Examples/DataRepresentation/Image/Image4.cxx
+++ b/Examples/DataRepresentation/Image/Image4.cxx
@@ -48,7 +48,7 @@
// \end{figure}
//
// Figure \ref{fig:ImageOriginAndSpacing} illustrates the main geometrical
-// concepts associated with the \doxygen{otb::Image}. In this figure,
+// concepts associated with the \doxygen{otb}{Image}. In this figure,
// circles are
// used to represent the center of pixels. The value of the pixel is assumed
// to exist as a Dirac Delta Function located at the pixel center. Pixel
@@ -203,7 +203,7 @@ int main(int, char *[])
// space can be mapped into an image index for the purpose of reading the
// content of the closest pixel.
//
- // First, a \doxygen{itk::Point} type must be declared. The point type is
+ // First, a \doxygen{itk}{Point} type must be declared. The point type is
// templated over the type used to represent coordinates and over the
// dimension of the space. In this particular case, the dimension of the
// point must match the dimension of the image.
@@ -217,10 +217,10 @@ int main(int, char *[])
// Software Guide : BeginLatex
//
- // The Point class, like an \doxygen{itk::Index}, is a relatively small and
+ // The Point class, like an \doxygen{itk}{Index}, is a relatively small and
// simple object. For this reason, it is not reference-counted like the
// large data objects in OTB. Consequently, it is also not manipulated
- // with \doxygen{itk::SmartPointer}s. Point objects are simply declared as
+ // with \doxygen{itk}{SmartPointer}s. Point objects are simply declared as
// instances of any other C++ class. Once the point is declared, its
// components can be accessed using traditional array notation. In
// particular, the \code{[]} operator is available. For efficiency reasons,
diff --git a/Examples/DataRepresentation/Image/Image5.cxx b/Examples/DataRepresentation/Image/Image5.cxx
index d35dfe875e..6ec76af561 100644
--- a/Examples/DataRepresentation/Image/Image5.cxx
+++ b/Examples/DataRepresentation/Image/Image5.cxx
@@ -24,11 +24,11 @@
// Software Guide : BeginLatex
//
-// This example illustrates how to import data into the \doxygen{otb::Image}
+// This example illustrates how to import data into the \doxygen{otb}{Image}
// class. This is particularly useful for interfacing with other software
// systems. Many systems use a contiguous block of memory as a buffer
// for image pixel data. The current example assumes this is the case and
-// feeds the buffer into an \doxygen{ImportImageFilter}, thereby producing an
+// feeds the buffer into an \doxygen{itk}{ImportImageFilter}, thereby producing an
// Image as output.
//
@@ -191,7 +191,7 @@ int main(int argc, char * argv[])
// programming languages. Note that ITK
// does not use \code{for()} loops in its internal code to access
// pixels. All pixel access tasks are instead performed using
- // \doxygen{itk::ImageIterator}s that support the management of
+ // \doxygen{itk}{ImageIterator}s that support the management of
// n-dimensional images.
//
// Software Guide : EndLatex
diff --git a/Examples/DataRepresentation/Image/RGBImage.cxx b/Examples/DataRepresentation/Image/RGBImage.cxx
index 2998036783..74ba4e38cb 100644
--- a/Examples/DataRepresentation/Image/RGBImage.cxx
+++ b/Examples/DataRepresentation/Image/RGBImage.cxx
@@ -35,7 +35,7 @@
//
// A class intended to support the RGB pixel type is available in ITK. You
// could also define your own pixel class and use it to instantiate a
-// custom image type. In order to use the \doxygen{itk::RGBPixel} class, it is
+// custom image type. In order to use the \doxygen{itk}{RGBPixel} class, it is
// necessary to include its header file.
//
// \index{itk::RGBPixel}
@@ -80,7 +80,7 @@ int main( int , char * argv[] )
// Software Guide : BeginLatex
//
// The image type can be used to instantiate other filter, for example,
- // an \doxygen{otb::ImageFileReader} object that will read the image from a
+ // an \doxygen{otb}{ImageFileReader} object that will read the image from a
// file.
//
// \index{otb::ImageFileReader!RGB Image}
@@ -138,8 +138,8 @@ int main( int , char * argv[] )
// Software Guide : BeginLatex
//
- // The subindex notation can also be used since the \doxygen{itk::RGBPixel} inherits the
- // \code{[]} operator from the \doxygen{itk::FixedArray} class.
+ // The subindex notation can also be used since the \doxygen{itk}{RGBPixel} inherits the
+ // \code{[]} operator from the \doxygen{itk}{FixedArray} class.
//
// Software Guide : EndLatex
diff --git a/Examples/DataRepresentation/Image/VectorImage.cxx b/Examples/DataRepresentation/Image/VectorImage.cxx
index 0d45ffe525..c773837a3f 100644
--- a/Examples/DataRepresentation/Image/VectorImage.cxx
+++ b/Examples/DataRepresentation/Image/VectorImage.cxx
@@ -28,23 +28,23 @@
// typical example is a multispectral image. The following code illustrates
// how to instantiate and use an image whose pixels are of vector type.
//
-// We could use the \doxygen{itk::Vector} class to define the pixel
+// We could use the \doxygen{itk}{Vector} class to define the pixel
// type. The Vector class is intended to represent a geometrical vector in
// space. It is not intended to be used as an array container like the
// \href{http://www.sgi.com/tech/stl/Vector.html}{\code{std::vector}} in
// \href{http://www.sgi.com/tech/stl/}{STL}. If you are interested in
-// containers, the \doxygen{VectorContainer} class may provide the
+// containers, the \doxygen{itk}{VectorContainer} class may provide the
// functionality you want.
//
// \index{itk::Vector}
// \index{itk::Vector!header}
//
-// However, the \doxygen{itk::Vector} is a fixed size array and it
+// However, the \doxygen{itk}{Vector} is a fixed size array and it
// assumes that the number of channels of the image is known at
// compile time. Therefore, we prefer to use the
-// \doxygen{otb::VectorImage} class which allows to choose the number
+// \doxygen{otb}{VectorImage} class which allows to choose the number
// of channels of the image at runtime. The pixels will be of type
-// \doxygen{itk::VariableLengthVector}.
+// \doxygen{itk}{VariableLengthVector}.
//
// The first step is to include the header file of the Vector class.
//
diff --git a/Examples/DataRepresentation/Mesh/Mesh1.cxx b/Examples/DataRepresentation/Mesh/Mesh1.cxx
index 776cb23472..7d2b971015 100644
--- a/Examples/DataRepresentation/Mesh/Mesh1.cxx
+++ b/Examples/DataRepresentation/Mesh/Mesh1.cxx
@@ -24,8 +24,8 @@
// Software Guide : BeginLatex
//
-// The \doxygen{itk::Mesh} class is intended to represent shapes in space. It
-// derives from the \doxygen{itk::PointSet} class and hence inherits all the
+// The \doxygen{itk}{Mesh} class is intended to represent shapes in space. It
+// derives from the \doxygen{itk}{PointSet} class and hence inherits all the
// functionality related to points and access to the pixel-data associated
// with the points. The mesh class is also n-dimensional which
// allows a great flexibility in its use.
@@ -101,7 +101,7 @@ int main(int, char *[])
// are reference counted objects and are managed using SmartPointers. The
// following line illustrates how a mesh is created by invoking the
// \code{New()} method of the MeshType and the resulting object is assigned
- // to a \doxygen{SmartPointer}.
+ // to a \doxygen{itk}{SmartPointer}.
//
// \index{itk::Mesh!New()}
// \index{itk::Mesh!Pointer()}
diff --git a/Examples/DataRepresentation/Mesh/Mesh2.cxx b/Examples/DataRepresentation/Mesh/Mesh2.cxx
index 1af6f963ae..2d78c5c972 100644
--- a/Examples/DataRepresentation/Mesh/Mesh2.cxx
+++ b/Examples/DataRepresentation/Mesh/Mesh2.cxx
@@ -24,10 +24,10 @@
// Software Guide : BeginLatex
//
-// A \doxygen{itk::Mesh} can contain a variety of cell types. Typical cells are
-// the \doxygen{itk::LineCell}, \doxygen{itk::TriangleCell},
-// \doxygen{itk::QuadrilateralCell} and
-// \doxygen{itk::TetrahedronCell}. The latter will not be used very
+// A \doxygen{itk}{Mesh} can contain a variety of cell types. Typical cells are
+// the \doxygen{itk}{LineCell}, \doxygen{itk}{TriangleCell},
+// \doxygen{itk}{QuadrilateralCell} and
+// \doxygen{itk}{TetrahedronCell}. The latter will not be used very
// often in the remote sensing context. Additional
// flexibility is provided for managing cells at the price of a bit more of
// complexity than in the case of point management.
@@ -109,7 +109,7 @@ int main(int, char *[])
// since it is now necessary to establish a protocol to make clear who is
// responsible for allocating and releasing the cells' memory. This protocol
// is implemented in the form of a specific type of pointer called the
- // \code{CellAutoPointer}. This pointer, based on the \doxygen{itk::AutoPointer},
+ // \code{CellAutoPointer}. This pointer, based on the \doxygen{itk}{AutoPointer},
// differs in many respects from the SmartPointer. The CellAutoPointer has an
// internal pointer to the actual object and a boolean flag that indicates
// if the CellAutoPointer is responsible for releasing the cell memory
diff --git a/Examples/DataRepresentation/Mesh/Mesh3.cxx b/Examples/DataRepresentation/Mesh/Mesh3.cxx
index 685f6092a7..665c890485 100644
--- a/Examples/DataRepresentation/Mesh/Mesh3.cxx
+++ b/Examples/DataRepresentation/Mesh/Mesh3.cxx
@@ -70,7 +70,7 @@ int main(int, char *[])
// Software Guide : BeginLatex
//
- // The \doxygen{itk::LineCell} type can now be instantiated using the traits
+ // The \doxygen{itk}{LineCell} type can now be instantiated using the traits
// taken from the Mesh.
//
// \index{itk::LineCell!Instantiation}
@@ -145,7 +145,7 @@ int main(int, char *[])
// Software Guide : BeginLatex
//
- // Data associated with cells is inserted in the \doxygen{itk::Mesh} by using
+ // Data associated with cells is inserted in the \doxygen{itk}{Mesh} by using
// the \code{SetCellData()} method. It requires the user to provide an
// identifier and the value to be inserted. The identifier should match one
// of the inserted cells. In this simple example, the square of the cell
diff --git a/Examples/DataRepresentation/Mesh/PointSet2.cxx b/Examples/DataRepresentation/Mesh/PointSet2.cxx
index 1cbfb2d442..8cf0fa2b0a 100644
--- a/Examples/DataRepresentation/Mesh/PointSet2.cxx
+++ b/Examples/DataRepresentation/Mesh/PointSet2.cxx
@@ -24,8 +24,8 @@
// Software Guide : BeginLatex
//
-// The \doxygen{PointSet} class uses an internal container to manage the storage of
-// \doxygen{Point}s. It is more efficient, in general, to manage points by using the
+// The \doxygen{itk}{PointSet} class uses an internal container to manage the storage of
+// \doxygen{itk}{Point}s. It is more efficient, in general, to manage points by using the
// access methods provided directly on the points container. The following
// example illustrates how to interact with the point container and how to use
// point iterators.
@@ -57,8 +57,8 @@ int main(int, char *[])
//
// The actual type of the PointsContainer depends on what style of
// PointSet is being used. The dynamic PointSet use the
- // \doxygen{itk::MapContainer} while the static PointSet uses the
- // \doxygen{itk::VectorContainer}. The vector and map containers are basically
+ // \doxygen{itk}{MapContainer} while the static PointSet uses the
+ // \doxygen{itk}{VectorContainer}. The vector and map containers are basically
// ITK wrappers around the \href{http://www.sgi.com/tech/stl/}{STL}
// classes \href{http://www.sgi.com/tech/stl/Map.html}{\code{std::map}}
// and \href{http://www.sgi.com/tech/stl/Vector.html}{\code{std::vector}}.
@@ -67,7 +67,7 @@ int main(int, char *[])
// and vector container are templated over the type of the elements they
// contain. In this case they are templated over PointType.
// Containers are reference counted object. They are then created with the
- // \code{New()} method and assigned to a \doxygen{SmartPointer} after
+ // \code{New()} method and assigned to a \doxygen{itk}{SmartPointer} after
// creation. The following line creates a point container compatible with
// the type of the PointSet from which the trait has been taken.
//
diff --git a/Examples/DataRepresentation/Mesh/PointSet3.cxx b/Examples/DataRepresentation/Mesh/PointSet3.cxx
index 88d9f8eb70..733bb08dc2 100644
--- a/Examples/DataRepresentation/Mesh/PointSet3.cxx
+++ b/Examples/DataRepresentation/Mesh/PointSet3.cxx
@@ -24,7 +24,7 @@
// Software Guide : BeginLatex
//
-// The \doxygen{itk::PointSet} class was designed to interact with the Image class.
+// The \doxygen{itk}{PointSet} class was designed to interact with the Image class.
// For this reason it was found convenient to allow the points in the set to
// hold values that could be computed from images. The value associated with
// the point is referred as \code{PixelType} in order to make it consistent
@@ -114,8 +114,8 @@ int main(int, char *[])
// Data associated with points is internally stored in
// \code{PointDataContainer}s. In the same way as with points, the actual
// container type used depend on whether the style of the PointSet is static
- // or dynamic. Static point sets will use an \doxygen{itk::VectorContainer} while
- // dynamic point sets will use an \doxygen{itk::MapContainer}. The type of the
+ // or dynamic. Static point sets will use an \doxygen{itk}{VectorContainer} while
+ // dynamic point sets will use an \doxygen{itk}{MapContainer}. The type of the
// data container is defined as one of the traits in the PointSet. The
// following declaration illustrates how the type can be taken from the
// traits and used to conveniently declare a similar type on the global
diff --git a/Examples/DataRepresentation/Mesh/PointSetWithVectors.cxx b/Examples/DataRepresentation/Mesh/PointSetWithVectors.cxx
index ee711dd3bd..080c0e915d 100644
--- a/Examples/DataRepresentation/Mesh/PointSetWithVectors.cxx
+++ b/Examples/DataRepresentation/Mesh/PointSetWithVectors.cxx
@@ -29,7 +29,7 @@
// points for producing geometric representations or storing
// multi-band informations. The following code shows
// how vector values can be used as pixel type on the PointSet class. The
-// \doxygen{itk::Vector} class is used here as the pixel type. This class is
+// \doxygen{itk}{Vector} class is used here as the pixel type. This class is
// appropriate for representing the relative position between two points. It
// could then be used to manage displacements in disparity map
// estimations, for example.
@@ -163,8 +163,8 @@ int main(int, char *[])
// Software Guide : BeginLatex
//
- // The \doxygen{itk::Vector} class has overloaded the \code{+} operator with
- // the \doxygen{itk::Point}. In other words, vectors can be added to points in
+ // The \doxygen{itk}{Vector} class has overloaded the \code{+} operator with
+ // the \doxygen{itk}{Point}. In other words, vectors can be added to points in
// order to produce new points. This property is exploited in the center
// of the loop in order to update the points positions with a single
// statement.
@@ -188,11 +188,11 @@ int main(int, char *[])
// Software Guide : BeginLatex
//
- // Note that \doxygen{itk::Vector} is not the appropriate class for
+ // Note that \doxygen{itk}{Vector} is not the appropriate class for
// representing normals to surfaces and gradients of functions. This is due
// to the way in which vectors behave under affine transforms. ITK has a
// specific class for representing normals and function gradients. This is
- // the \doxygen{itk::CovariantVector} class.
+ // the \doxygen{itk}{CovariantVector} class.
//
// Software Guide : EndLatex
diff --git a/Examples/DataRepresentation/Path/PolyLineParametricPath1.cxx b/Examples/DataRepresentation/Path/PolyLineParametricPath1.cxx
index 38a1dce101..86f4f4a82a 100644
--- a/Examples/DataRepresentation/Path/PolyLineParametricPath1.cxx
+++ b/Examples/DataRepresentation/Path/PolyLineParametricPath1.cxx
@@ -24,7 +24,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates how to use the \doxygen{PolyLineParametricPath}.
+// This example illustrates how to use the \doxygen{itk}{PolyLineParametricPath}.
// This class will typically be used for representing in a concise way the
// output of an image segmentation algorithm in 2D. See section
// \ref{sec:Alignments} for an example in the context of alignment
diff --git a/Examples/FeatureExtraction/AlignmentsExample.cxx b/Examples/FeatureExtraction/AlignmentsExample.cxx
index 1ed1ca110e..a85045a4fe 100644
--- a/Examples/FeatureExtraction/AlignmentsExample.cxx
+++ b/Examples/FeatureExtraction/AlignmentsExample.cxx
@@ -31,7 +31,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{ImageToPathListAlignFilter}.
+// This example illustrates the use of the \doxygen{otb}{ImageToPathListAlignFilter}.
// This filter allows to extract meaninful alignments. Alignments
// (that is edges and lines) are detected using the {\em Gestalt}
// approach proposed by Desolneux et al. \cite{desolneux}. In this
@@ -55,7 +55,7 @@
// Software Guide : BeginLatex
// In order to visualize the detected alignments, we will use the
-// facility class \doxygen{DrawPathFilter} which draws a
+// facility class \doxygen{otb}{DrawPathFilter} which draws a
// \code{itk::PolyLineParametricPath} on top of a given image.
// Software Guide : EndLatex
@@ -108,7 +108,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{ImageToPathListAlignFilter} is templated over the
+ // The \doxygen{otb}{ImageToPathListAlignFilter} is templated over the
// input image type and the output path type, so we start by
// defining:
//
@@ -148,7 +148,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
- // As stated, above, the \doxygen{DrawPathFilter}, is useful for
+ // As stated, above, the \doxygen{otb}{DrawPathFilter}, is useful for
// drawint the detected alignments. This class is templated over
// the input image and path types and also on the output image
// type.
@@ -163,7 +163,7 @@ int main( int argc, char ** argv )
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// We will now go through the list of detected paths and feed them
- // to the \doxygen{DrawPathFilter} inside a loop. We will use a list
+ // to the \doxygen{otb}{DrawPathFilter} inside a loop. We will use a list
// iterator inside a \code{while} statement.
// Software Guide : BeginCodeSnippet
@@ -179,7 +179,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
// We define a dummy image will be iteratively fed to the
- // \doxygen{DrawPathFilter} after the drawing of each alignment.
+ // \doxygen{otb}{DrawPathFilter} after the drawing of each alignment.
//
// Software Guide : EndLatex
@@ -222,7 +222,7 @@ int main( int argc, char ** argv )
// \includegraphics[width=0.35\textwidth]{QB_Suburb.eps}
// \includegraphics[width=0.35\textwidth]{QB_SuburbAlign.eps}
// \itkcaption[Lee Filter Application]{Result of applying the
- // \doxygen{ImageToPathListAlignFilter} to a VHR image of a suburb.}
+ // \doxygen{otb}{ImageToPathListAlignFilter} to a VHR image of a suburb.}
// \label{fig:Align}
// \end{figure}
//
@@ -230,7 +230,7 @@ int main( int argc, char ** argv )
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{FrostImageFilter}
+ // \item \doxygen{otb}{FrostImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx b/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx
index 6fe53c8a52..cb173be2f1 100644
--- a/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx
+++ b/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::AssymmetricFusionOfLineDetectorImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{AssymmetricFusionOfLineDetectorImageFilter}.
//
// The first step required to use this filter is to include its header file.
//
@@ -95,7 +95,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -106,7 +106,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -175,7 +175,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::AssymetricFusionOfDetectorImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{AssymetricFusionOfDetectorImageFilter}. The pipeline is built as follows.
//
// \index{otb::AssymetricFusionOfDetectorImageFilter!SetInput()}
//
@@ -233,7 +233,7 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.25\textwidth]{amst.eps}
// \includegraphics[width=0.25\textwidth]{amstLineFusion.eps}
// \itkcaption[Line Correlation Detector Application]{Result of applying
- // the \doxygen{otb::AssymetricFusionOfDetectorImageFilter} to a SAR
+ // the \doxygen{otb}{AssymetricFusionOfDetectorImageFilter} to a SAR
// image. From left to right : original image, line intensity.} \label{fig:LINEFUSION_FILTER} \end{figure}
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/ComplexMomentImageExample.cxx b/Examples/FeatureExtraction/ComplexMomentImageExample.cxx
index f05b628c37..55d52ddd33 100644
--- a/Examples/FeatureExtraction/ComplexMomentImageExample.cxx
+++ b/Examples/FeatureExtraction/ComplexMomentImageExample.cxx
@@ -33,7 +33,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::ComplexMomentImageFunction}.
+// This example illustrates the use of the \doxygen{otb}{ComplexMomentImageFunction}.
//
// The first step required to use this filter is to include its header file.
//
@@ -70,7 +70,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::ComplexMomentImageFunction} is templated over the
+ // The \doxygen{otb}{ComplexMomentImageFunction} is templated over the
// input image type and the output complex type value, so we start by
// defining:
//
diff --git a/Examples/FeatureExtraction/ComplexMomentPathExample.cxx b/Examples/FeatureExtraction/ComplexMomentPathExample.cxx
index ee6cce05aa..eaf5c22848 100644
--- a/Examples/FeatureExtraction/ComplexMomentPathExample.cxx
+++ b/Examples/FeatureExtraction/ComplexMomentPathExample.cxx
@@ -35,8 +35,8 @@
// The complex moments can be computed on images, but sometimes we are
// interested in computing them on shapes extracted from images by
// segmentation algorithms. These shapes can be represented by
-// \doxygen{itk::Path}s. This example illustrates the use of the
-// \doxygen{otb::ComplexMomentPathFunction} for the computation of
+// \doxygen{itk}{Path}s. This example illustrates the use of the
+// \doxygen{otb}{ComplexMomentPathFunction} for the computation of
// complex geometric moments on ITK paths.
//
// The first step required to use this filter is to include its header file.
@@ -66,7 +66,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::ComplexMomentPathFunction} is templated over the
+ // The \doxygen{otb}{ComplexMomentPathFunction} is templated over the
// input path type and the output complex type value, so we start by
// defining:
//
@@ -133,7 +133,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
// Since the paths are defined in physical coordinates, we do not
// need to set the center for the moment computation as we did
- // with the \doxygen{otb::ComplexMomentImageFunction}. The same
+ // with the \doxygen{otb}{ComplexMomentImageFunction}. The same
// applies for the size of the neighborhood around the
// center pixel for the moment computation. The moment computation
// is triggered by calling the \code{Evaluate} method.
diff --git a/Examples/FeatureExtraction/CorrelationLineDetectorExample.cxx b/Examples/FeatureExtraction/CorrelationLineDetectorExample.cxx
index 956f4abbe8..7b7bdd9c0a 100644
--- a/Examples/FeatureExtraction/CorrelationLineDetectorExample.cxx
+++ b/Examples/FeatureExtraction/CorrelationLineDetectorExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::CorrelationLineDetectorImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{CorrelationLineDetectorImageFilter}.
// This filter is used for line detection in SAR images. Its principle
// is described in \cite{tup-98}: a line is detected if two parallel
// edges are present in the images. These edges are detected with the
@@ -99,7 +99,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -110,7 +110,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -179,7 +179,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::LineCorrelationDetectorImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{LineCorrelationDetectorImageFilter}. The pipeline is built as follows.
//
// \index{otb::LineCorrelationDetectorImageFilter!SetInput()}
//
@@ -248,13 +248,13 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.25\textwidth]{amstLineCorrelations.eps}
// \includegraphics[width=0.25\textwidth]{amstLineCorrelationDirections.eps}
// \itkcaption[Line Correlation Detector Application]{Result of applying
- // the \doxygen{otb::LineCorrelationDetectorImageFilter} to a SAR
+ // the \doxygen{otb}{LineCorrelationDetectorImageFilter} to a SAR
// image. From left to right : original image, line intensity and
// edge orientation.} \label{fig:LINECORRELATION_FILTER} \end{figure}
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::LineCorrelationDetectorImageFilter}
+ // \item \doxygen{otb}{LineCorrelationDetectorImageFilter}
// \end{itemize}
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/ExtractSegmentsByStepsExample.cxx b/Examples/FeatureExtraction/ExtractSegmentsByStepsExample.cxx
index a420413457..92e2c07a1b 100644
--- a/Examples/FeatureExtraction/ExtractSegmentsByStepsExample.cxx
+++ b/Examples/FeatureExtraction/ExtractSegmentsByStepsExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::ExtractSegmentsImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{ExtractSegmentsImageFilter}.
//
// The first step required to use this filter is to include its header file.
//
@@ -119,7 +119,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -130,7 +130,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -180,7 +180,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::ExtractSegmentsImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{ExtractSegmentsImageFilter}. The pipeline is built as follows.
//
// \index{otb::ExtractSegmentsImageFilter!SetInput()}
//
@@ -282,13 +282,13 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.25\textwidth]{amst.eps}
// \includegraphics[width=0.25\textwidth]{amstSegmentExtractionBySteps.eps}
// \itkcaption[Line Correlation Detector Application]{Result of applying
- // the \doxygen{otb::AssymetricFusionOfDetectorImageFilter} to a SAR
+ // the \doxygen{otb}{AssymetricFusionOfDetectorImageFilter} to a SAR
// image. From left to right : original image, line intensity and
// edge orientation.} \label{fig:LINECORRELATION_FILTER} \end{figure}
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::AssymetricFusionOfDetectorImageFilter}
+ // \item \doxygen{otb}{AssymetricFusionOfDetectorImageFilter}
// \end{itemize}
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/ExtractSegmentsExample.cxx b/Examples/FeatureExtraction/ExtractSegmentsExample.cxx
index 3bfadc8b43..0ade3bbcd5 100644
--- a/Examples/FeatureExtraction/ExtractSegmentsExample.cxx
+++ b/Examples/FeatureExtraction/ExtractSegmentsExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::ExtractSegmentsImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{ExtractSegmentsImageFilter}.
//
// The first step required to use this filter is to include its header file.
//
@@ -186,7 +186,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::ExtractSegmentsImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{ExtractSegmentsImageFilter}. The pipeline is built as follows.
//
// \index{otb::ExtractSegmentsImageFilter!SetInput()}
//
@@ -278,13 +278,13 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.25\textwidth]{amst.eps}
// \includegraphics[width=0.25\textwidth]{amstSegmentExtraction.eps}
// \itkcaption[Line Correlation Detector Application]{Result of applying
- // the \doxygen{otb::AssymetricFusionOfDetectorImageFilter} to a SAR
+ // the \doxygen{otb}{AssymetricFusionOfDetectorImageFilter} to a SAR
// image. From left to right : original image, line intensity and
// edge orientation.} \label{fig:LINECORRELATION_FILTER} \end{figure}
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::AssymetricFusionOfDetectorImageFilter}
+ // \item \doxygen{otb}{AssymetricFusionOfDetectorImageFilter}
// \end{itemize}
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/FlusserMomentImageExample.cxx b/Examples/FeatureExtraction/FlusserMomentImageExample.cxx
index da9d35ad98..79f415c409 100644
--- a/Examples/FeatureExtraction/FlusserMomentImageExample.cxx
+++ b/Examples/FeatureExtraction/FlusserMomentImageExample.cxx
@@ -33,7 +33,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::FlusserMomentImageFunction}.
+// This example illustrates the use of the \doxygen{otb}{FlusserMomentImageFunction}.
//
// The first step required to use this filter is to include its header file.
//
@@ -70,7 +70,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::FlusserImageFunction} is templated over the
+ // The \doxygen{otb}{FlusserImageFunction} is templated over the
// input image type and the output (real) type value, so we start by
// defining:
//
@@ -160,7 +160,7 @@ int main(int argc, char ** argv )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::FlusserPathFunction}
+ // \item \doxygen{otb}{FlusserPathFunction}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/HarrisExample.cxx b/Examples/FeatureExtraction/HarrisExample.cxx
index cdac1ce1ec..d9ad71147b 100644
--- a/Examples/FeatureExtraction/HarrisExample.cxx
+++ b/Examples/FeatureExtraction/HarrisExample.cxx
@@ -34,7 +34,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{HarrisImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{HarrisImageFilter}.
//
// The first step required to use this filter is to include its header file.
//
@@ -73,7 +73,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{HarrisImageFilter} is templated over the
+ // The \doxygen{otb}{HarrisImageFilter} is templated over the
// input and output image types, so we start by
// defining:
//
@@ -102,7 +102,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{HarrisImageFilter} needs some parameters to
+ // The \doxygen{otb}{HarrisImageFilter} needs some parameters to
// operate. The derivative computation is performed by a
// convolution with the derivative of a Gaussian kernel of
// variance $\sigma_D$ (derivation scale) and
@@ -139,18 +139,18 @@ int main(int argc, char ** argv )
// \includegraphics[width=0.25\textwidth]{ROISpot5.eps}
// \includegraphics[width=0.25\textwidth]{ROISpot5Harris.eps}
// \itkcaption[Lee Filter Application]{Result of applying the
- // \doxygen{otb::HarrisImageFilter} to a Spot 5 image.}
+ // \doxygen{otb}{HarrisImageFilter} to a Spot 5 image.}
// \label{fig:Harris}
// \end{figure}
//
- // The output of the \doxygen{otb::HarrisImageFilter} is an image
+ // The output of the \doxygen{otb}{HarrisImageFilter} is an image
// where,for each pixel, we obtain the intensity of the
// detection. Often, the user may want to get access to the set of
// points for which the output of the detector is higher than a
// given threshold. This can be obtained by using the
- // \doxygen{otb::HarrisImageToPointSetFilter}. This filter is only
+ // \doxygen{otb}{HarrisImageToPointSetFilter}. This filter is only
// templated over the input image type, the output being a
- // \doxygen{itk::PointSet} with pixel type equal to the image pixel type.
+ // \doxygen{itk}{PointSet} with pixel type equal to the image pixel type.
//
// Software Guide : EndLatex
@@ -172,8 +172,8 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::HarrisImageToPointSetFilter} takes the same
- // parameters as the \doxygen{otb::HarrisImageFilter} and an
+ // The \doxygen{otb}{HarrisImageToPointSetFilter} takes the same
+ // parameters as the \doxygen{otb}{HarrisImageFilter} and an
// additional parameter : the threshold for the point selection.
//
// Software Guide : EndLatex
@@ -197,7 +197,7 @@ int main(int argc, char ** argv )
// the coordinates of the points. We start by accessing the
// container of the points which is encapsulated into the point
// set (see section \ref{sec:PointSetSection} for more
- // information on using \doxygen{itk::PointSet}s) and declaring
+ // information on using \doxygen{itk}{PointSet}s) and declaring
// an iterator to it.
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/HuMomentImageExample.cxx b/Examples/FeatureExtraction/HuMomentImageExample.cxx
index 071f10d777..8629793022 100644
--- a/Examples/FeatureExtraction/HuMomentImageExample.cxx
+++ b/Examples/FeatureExtraction/HuMomentImageExample.cxx
@@ -33,7 +33,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::HuMomentImageFunction}.
+// This example illustrates the use of the \doxygen{otb}{HuMomentImageFunction}.
//
// The first step required to use this filter is to include its header file.
//
@@ -70,7 +70,7 @@ int main(int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{otb::HuImageFunction} is templated over the
+ // The \doxygen{otb}{HuImageFunction} is templated over the
// input image type and the output (real) type value, so we start by
// defining:
//
@@ -160,7 +160,7 @@ int main(int argc, char ** argv )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::HuPathFunction}
+ // \item \doxygen{otb}{HuPathFunction}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/LocalHoughExample.cxx b/Examples/FeatureExtraction/LocalHoughExample.cxx
index e2d2225792..852a5e0578 100644
--- a/Examples/FeatureExtraction/LocalHoughExample.cxx
+++ b/Examples/FeatureExtraction/LocalHoughExample.cxx
@@ -24,7 +24,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::ExtractSegmentsImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{ExtractSegmentsImageFilter}.
//
// The first step required to use this filter is to include its header file.
//
@@ -106,7 +106,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -117,7 +117,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -185,7 +185,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::ExtractSegmentsImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{ExtractSegmentsImageFilter}. The pipeline is built as follows.
//
// \index{otb::ExtractSegmentsImageFilter!SetInput()}
//
@@ -209,12 +209,12 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
// Figure~\ref{fig:LOCAL_HOUGH}
- // shows the result of applying the \doxygen{otb::LocalHoughImageFilter}.
+ // shows the result of applying the \doxygen{otb}{LocalHoughImageFilter}.
// \begin{figure} \center
// \includegraphics[width=0.25\textwidth]{detected_lines.eps}
// \includegraphics[width=0.25\textwidth]{detected_local_hough.eps}
// \itkcaption[Line Correlation Detector Application]{Result of applying
- // the \doxygen{otb::LocalHoughImageFilter}. From left to right :
+ // the \doxygen{otb}{LocalHoughImageFilter}. From left to right :
// original image, extracted segments.} \label{fig:LOCAL_HOUGH} \end{figure}
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/RatioLineDetectorExample.cxx b/Examples/FeatureExtraction/RatioLineDetectorExample.cxx
index 2f9136194b..54a3f859b0 100644
--- a/Examples/FeatureExtraction/RatioLineDetectorExample.cxx
+++ b/Examples/FeatureExtraction/RatioLineDetectorExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::RatioLineDetectorImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{RatioLineDetectorImageFilter}.
// This filter is used for line detection in SAR images. Its principle
// is described in \cite{tup-98}: a line is detected if two parallel
// edges are present in the images. These edges are detected with the
@@ -99,7 +99,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -110,7 +110,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -179,7 +179,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::LineRatioDetectorImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{LineRatioDetectorImageFilter}. The pipeline is built as follows.
//
// \index{otb::LineRatioDetectorImageFilter!SetInput()}
//
@@ -248,13 +248,13 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.25\textwidth]{amstLineRatios.eps}
// \includegraphics[width=0.25\textwidth]{amstLineRatioDirections.eps}
// \itkcaption[Line Ratio Detector Application]{Result of applying
- // the \doxygen{otb::LineRatioDetectorImageFilter} to a SAR
+ // the \doxygen{otb}{LineRatioDetectorImageFilter} to a SAR
// image. From left to right : original image, line intensity and
// edge orientation.} \label{fig:LINERATIO_FILTER} \end{figure}
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{otb::LineCorrelationDetectorImageFilter}
+ // \item \doxygen{otb}{LineCorrelationDetectorImageFilter}
// \end{itemize}
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/TouziEdgeDetectorExample.cxx b/Examples/FeatureExtraction/TouziEdgeDetectorExample.cxx
index 96379bdac0..2b538ebf8e 100644
--- a/Examples/FeatureExtraction/TouziEdgeDetectorExample.cxx
+++ b/Examples/FeatureExtraction/TouziEdgeDetectorExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates the use of the \doxygen{otb::TouziEdgeDetectorImageFilter}.
+// This example illustrates the use of the \doxygen{otb}{TouziEdgeDetectorImageFilter}.
// This filter belongs to the family of the fixed false alarm rate
// edge detectors but it is apropriate for SAR images, where the
// speckle noise is considered as multiplicative. By analogy with the
@@ -189,7 +189,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{otb::TouziEdgeDetectorImageFilter}. The pipeline is built as follows.
+ // \doxygen{otb}{TouziEdgeDetectorImageFilter}. The pipeline is built as follows.
//
// \index{otb::TouziEdgeDetectorImageFilter!SetInput()}
//
@@ -263,7 +263,7 @@ int main( int argc, char * argv[] )
// \includegraphics[width=0.25\textwidth]{amstTouziEdges.eps}
// \includegraphics[width=0.25\textwidth]{amstTouziDirections.eps}
// \itkcaption[Touzi Edge Detector Application]{Result of applying the
- // \doxygen{otb::TouziEdgeDetectorImageFilter} to a SAR image. From left to right :
+ // \doxygen{otb}{TouziEdgeDetectorImageFilter} to a SAR image. From left to right :
// original image, edge intensity and edge orientation.}
// \label{fig:TOUZI_FILTER}
// \end{figure}
diff --git a/Examples/Filtering/BinaryThresholdImageFilter.cxx b/Examples/Filtering/BinaryThresholdImageFilter.cxx
index 0a1ac6cc29..6900d0a758 100644
--- a/Examples/Filtering/BinaryThresholdImageFilter.cxx
+++ b/Examples/Filtering/BinaryThresholdImageFilter.cxx
@@ -55,7 +55,7 @@
// \index{itk::Binary\-Threshold\-Image\-Filter!Instantiation}
// \index{itk::Binary\-Threshold\-Image\-Filter!Header}
//
-// The first step required to use the \doxygen{itk::BinaryThresholdImageFilter} is
+// The first step required to use the \doxygen{itk}{BinaryThresholdImageFilter} is
// to include its header file.
//
// Software Guide : EndLatex
@@ -120,7 +120,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file. (See Section \ref{sec:IO} on page
// \pageref{sec:IO} for more information about reading
// and writing data.)
@@ -134,7 +134,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileWriter} is instantiated in order to write the output
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the output
// image to a file.
//
// Software Guide : EndLatex
@@ -148,7 +148,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// Both the filter and the reader are created by invoking their \code{New()}
- // methods and assigning the result to \doxygen{SmartPointer}s.
+ // methods and assigning the result to \doxygen{itk}{SmartPointer}s.
//
// Software Guide : EndLatex
@@ -253,7 +253,7 @@ int main( int argc, char * argv[] )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{itk::ThresholdImageFilter}
+ // \item \doxygen{itk}{ThresholdImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/Filtering/CannyEdgeDetectionImageFilter.cxx b/Examples/Filtering/CannyEdgeDetectionImageFilter.cxx
index 89fea6ed9e..8801a623c5 100755
--- a/Examples/Filtering/CannyEdgeDetectionImageFilter.cxx
+++ b/Examples/Filtering/CannyEdgeDetectionImageFilter.cxx
@@ -36,7 +36,7 @@
// Software Guide : BeginLatex
//
// This example introduces the use of the
-// \doxygen{itk::CannyEdgeDetectionImageFilter}. This filter is widely used for
+// \doxygen{itk}{CannyEdgeDetectionImageFilter}. This filter is widely used for
// edge detection since it is the optimal solution satisfying the constraints
// of good sensitivity, localization and noise robustness.
//
@@ -101,7 +101,7 @@ int main(int argc, char* argv[])
//
// This filter operates on image of pixel type float. It is then necessary
// to cast the type of the input images that are usually of integer type.
- // The \doxygen{itk::CastImageFilter} is used here for that purpose. Its image
+ // The \doxygen{itk}{CastImageFilter} is used here for that purpose. Its image
// template parameters are defined for casting from the input type to the
// float type using for processing.
//
@@ -118,7 +118,7 @@ int main(int argc, char* argv[])
// Software Guide : BeginLatex
//
- // The \doxygen{itk::CannyEdgeDetectionImageFilter} is instantiated using the float image type.
+ // The \doxygen{itk}{CannyEdgeDetectionImageFilter} is instantiated using the float image type.
//
// \index{itk::CannyEdgeDetectionImageFilter|textbf}
//
diff --git a/Examples/Filtering/ThresholdImageFilter.cxx b/Examples/Filtering/ThresholdImageFilter.cxx
index 0ee6df27f8..ac3a04a267 100644
--- a/Examples/Filtering/ThresholdImageFilter.cxx
+++ b/Examples/Filtering/ThresholdImageFilter.cxx
@@ -62,7 +62,7 @@
// \label{fig:ThresholdTransferFunctionOutside}
// \end{figure}
//
-// This example illustrates the use of the \doxygen{itk::ThresholdImageFilter}.
+// This example illustrates the use of the \doxygen{itk}{ThresholdImageFilter}.
// This filter can be used to transform the intensity levels of an image in
// three different ways.
//
@@ -160,7 +160,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file.
//
// Software Guide : EndLatex
@@ -171,7 +171,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileWriter} is instantiated in order to write the
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the
// output image to a file.
//
// Software Guide : EndLatex
@@ -202,7 +202,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// The image obtained with the reader is passed as input to the
- // \doxygen{itk::ThresholdImageFilter}.
+ // \doxygen{itk}{ThresholdImageFilter}.
//
// \index{itk::ThresholdImageFilter!SetInput()}
// \index{itk::FileImageReader!GetOutput()}
@@ -310,7 +310,7 @@ int main( int argc, char * argv[] )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{itk::BinaryThresholdImageFilter}
+ // \item \doxygen{itk}{BinaryThresholdImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/IO/ExtractROI.cxx b/Examples/IO/ExtractROI.cxx
index c1fe33455d..4ceed23b3f 100755
--- a/Examples/IO/ExtractROI.cxx
+++ b/Examples/IO/ExtractROI.cxx
@@ -36,12 +36,12 @@
// Software Guide : BeginLatex
//
// This example shows the use of the
-// \doxygen{otb::MultiChannelExtractROI} and
-// \doxygen{otb::MultiToMonoChannelExtractROI} which allow the
+// \doxygen{otb}{MultiChannelExtractROI} and
+// \doxygen{otb}{MultiToMonoChannelExtractROI} which allow the
// extraction of ROIs from multiband images stored into
-// \doxygen{otb::VectorImage}s. The first one povides a Vector Image
+// \doxygen{otb}{VectorImage}s. The first one povides a Vector Image
// as output, while the second one provides a classical
-// \doxygen{otb::Image} with a scalar pixel type. The present example
+// \doxygen{otb}{Image} with a scalar pixel type. The present example
// shows how to extract a ROI from a 4-band SPOT 5 image and to
// produce a first multi-band 3-channel image and a second
// mono-channel one for the SWIR band.
@@ -109,10 +109,10 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// First of all, we extract the multiband part by using the
- // \doxygen{otb::MultiChannelExtractROI} class, which is templated
+ // \doxygen{otb}{MultiChannelExtractROI} class, which is templated
// over the input and output pixel types. This class in not
// templated over the images types in order to force these images
- // to be of \doxygen{otb::VectorImage} type.
+ // to be of \doxygen{otb}{VectorImage} type.
//
// Software Guide : EndLatex
@@ -205,18 +205,18 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // The usage of the \doxygen{otb::MultiToMonoChannelExtractROI} is
- // similar to the one of the \doxygen{otb::MultiChannelExtractROI}
+ // The usage of the \doxygen{otb}{MultiToMonoChannelExtractROI} is
+ // similar to the one of the \doxygen{otb}{MultiChannelExtractROI}
// described above.
//
// The goal now is to extract an ROI from a multi-band image and
// generate a mono-channel image as output.
//
- // We could use the \doxygen{otb::MultiChannelExtractROI} and
+ // We could use the \doxygen{otb}{MultiChannelExtractROI} and
// select a single channel, but using the
- // \doxygen{otb::MultiToMonoChannelExtractROI} we generate a
- // \doxygen{otb::Image} instead of an
- // \doxygen{otb::VectorImage}. This is useful from a computing and
+ // \doxygen{otb}{MultiToMonoChannelExtractROI} we generate a
+ // \doxygen{otb}{Image} instead of an
+ // \doxygen{otb}{VectorImage}. This is useful from a computing and
// memory usage point of view.
// This class is also templated over the pixel types.
//
diff --git a/Examples/IO/ImageReadCastWrite.cxx b/Examples/IO/ImageReadCastWrite.cxx
index 734ad65a60..4ffa064b8d 100644
--- a/Examples/IO/ImageReadCastWrite.cxx
+++ b/Examples/IO/ImageReadCastWrite.cxx
@@ -35,7 +35,7 @@
// reading an image of one pixel type and writing it on a different pixel
// type. This process not only involves casting but also rescaling the image
// intensity since the dynamic range of the input and output pixel types can
-// be quite different. The \doxygen{itk::RescaleIntensityImageFilter} is used
+// be quite different. The \doxygen{itk}{RescaleIntensityImageFilter} is used
// here to linearly rescale the image values.
//
// The first step in this example is to include the appropriate headers.
diff --git a/Examples/IO/ImageReadRegionOfInterestWrite.cxx b/Examples/IO/ImageReadRegionOfInterestWrite.cxx
index c8da2bb89c..d2a1923d76 100644
--- a/Examples/IO/ImageReadRegionOfInterestWrite.cxx
+++ b/Examples/IO/ImageReadRegionOfInterestWrite.cxx
@@ -47,7 +47,7 @@
// Software Guide : BeginLatex
//
-// The \doxygen{otb::ExtractROI} is the filter used to extract a
+// The \doxygen{otb}{ExtractROI} is the filter used to extract a
// region from an image. Its header is included below.
//
// \index{otb::ExtractROI!header}
@@ -90,7 +90,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The types for the \doxygen{otb::ImageFileReader} and \doxygen{otb::ImageFileWriter}
+ // The types for the \doxygen{otb}{ImageFileReader} and \doxygen{otb}{ImageFileWriter}
// are instantiated using the image types.
//
// Software Guide : EndLatex
@@ -106,11 +106,11 @@ int main( int argc, char ** argv )
// The ExtractROI type is instantiated using
// the input and output pixel types. Using the pixel types as
// template parameters instead of the image types allows to
- // restrict the use of this class to \doxygen{otb::Image}s which
+ // restrict the use of this class to \doxygen{otb}{Image}s which
// are used with scalar pixel types. See section
// \ref{sec:ExtractROI} for the extraction of ROIs on
- // \doxygen{otb::VectorImage}s. A filter object is created with the
- // New() method and assigned to a \doxygen{SmartPointer}.
+ // \doxygen{otb}{VectorImage}s. A filter object is created with the
+ // New() method and assigned to a \doxygen{itk}{SmartPointer}.
//
// Software Guide : EndLatex
@@ -128,7 +128,7 @@ int main( int argc, char ** argv )
// defined by the user. This is done by defining a rectangle with
// the following methods (the filter assumes that a 2D image is
// being processed, for N-D region extraction, you can use the
- // \doxygen{itk::RegionOfInterestImageFilter} class).
+ // \doxygen{itk}{RegionOfInterestImageFilter} class).
//
// Software Guide : EndLatex
diff --git a/Examples/IO/ImageReadWrite.cxx b/Examples/IO/ImageReadWrite.cxx
index 11d088a71d..edcff9a4d0 100644
--- a/Examples/IO/ImageReadWrite.cxx
+++ b/Examples/IO/ImageReadWrite.cxx
@@ -34,12 +34,12 @@
// Generally speaking they are referred to as filters, although readers have
// no pipeline input and writers have no pipeline output.
//
-// The reading of images is managed by the class \doxygen{otb::ImageFileReader}
-// while writing is performed by the class \doxygen{otb::ImageFileWriter}. These
+// The reading of images is managed by the class \doxygen{otb}{ImageFileReader}
+// while writing is performed by the class \doxygen{otb}{ImageFileWriter}. These
// two classes are independent of any particular file format. The actual low
// level task of reading and writing specific file formats is done behind
// the scenes by a family of classes of type
-// \doxygen{itk::ImageIO}. Actually, the OTB image Readers and
+// \doxygen{itk}{ImageIO}. Actually, the OTB image Readers and
// Writers are very similar to those of ITK, but provide new
// functionnalities which are specific to remote sensing images.
//
@@ -129,7 +129,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
// Then, we create one object of each type using the New() method and
- // assigning the result to a \doxygen{SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// \index{otb::ImageFileReader!New()}
// \index{otb::ImageFileWriter!New()}
@@ -225,7 +225,7 @@ int main( int argc, char ** argv )
// extension, but the architecture supports arbitrarily complex processes
// to determine whether a file can be read or written. Alternatively, the
// user can specify the data file format by explicit instantiation and
- // assignment the appropriate \doxygen{itk::ImageIO} subclass.
+ // assignment the appropriate \doxygen{itk}{ImageIO} subclass.
//
//
// Software Guide : EndLatex
diff --git a/Examples/IO/MetadataExample.cxx b/Examples/IO/MetadataExample.cxx
index 2014515029..2eb4d1d7f2 100755
--- a/Examples/IO/MetadataExample.cxx
+++ b/Examples/IO/MetadataExample.cxx
@@ -32,9 +32,9 @@
// CEOS and GeoTiff.
//
// The metadata support is embedded in OTB's IO functionnalities and
-// is accessible through the \doxygen{otb::Image} and
-// \doxygen{otb::VectorImage} classes. You should avoid using the
-// \doxygen{itk::Image} class if you want to have metadata support.
+// is accessible through the \doxygen{otb}{Image} and
+// \doxygen{otb}{VectorImage} classes. You should avoid using the
+// \doxygen{itk}{Image} class if you want to have metadata support.
//
// SoftwareGuide: EndLatex
diff --git a/Examples/IO/MultibandImageReadWrite.cxx b/Examples/IO/MultibandImageReadWrite.cxx
index 7b18273fb9..f3b21d8416 100644
--- a/Examples/IO/MultibandImageReadWrite.cxx
+++ b/Examples/IO/MultibandImageReadWrite.cxx
@@ -25,17 +25,17 @@
// Software Guide : BeginLatex
//
-// The \doxygen{otb::Image} class with a vector pixel type could be
+// The \doxygen{otb}{Image} class with a vector pixel type could be
// used for representing multispectral images, with one band per
// vector component, however, this is not a practical way, since the
// dimensionality of the vector must be known at compile time. OTB
-// offers the \doxygen{otb::VectorImage} where the dimensionality of
+// offers the \doxygen{otb}{VectorImage} where the dimensionality of
// the vector stored for each pixel can be chosen at runtime. This is
// needed for the image file readers in order to dynamically set the
// number of bands of an image read from a file.
//
// The OTB Readers and Writers are able to deal with
-// \doxygen{otb::VectorImage}s transparently for the user.
+// \doxygen{otb}{VectorImage}s transparently for the user.
//
// The first step for performing reading and writing is to include the
// following headers.
@@ -106,7 +106,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
// Then, we create one object of each type using the New() method and
- // assigning the result to a \doxygen{SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// \index{otb::ImageFileReader!New()}
// \index{otb::ImageFileWriter!New()}
diff --git a/Examples/IO/RGBImageReadWrite.cxx b/Examples/IO/RGBImageReadWrite.cxx
index 3021dc6132..d401b147bf 100644
--- a/Examples/IO/RGBImageReadWrite.cxx
+++ b/Examples/IO/RGBImageReadWrite.cxx
@@ -60,7 +60,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
- // The \doxygen{itk::RGBPixel} class is templated over the type used to
+ // The \doxygen{itk}{RGBPixel} class is templated over the type used to
// represent each one of the red, green and blue components. A typical
// instantiation of the RGB image class might be as follows.
//
@@ -130,7 +130,7 @@ int main( int argc, char ** argv )
// You may have noticed that apart from the declaration of the
// \code{PixelType} there is nothing in this code that is specific for RGB
// images. All the actions required to support color images are implemented
- // internally in the \doxygen{itk::ImageIO} objects.
+ // internally in the \doxygen{itk}{ImageIO} objects.
//
// Software Guide : EndLatex
diff --git a/Examples/IO/StreamingImageReadWrite.cxx b/Examples/IO/StreamingImageReadWrite.cxx
index 830c49f797..84a2c6c46e 100644
--- a/Examples/IO/StreamingImageReadWrite.cxx
+++ b/Examples/IO/StreamingImageReadWrite.cxx
@@ -25,16 +25,16 @@
// Software Guide : BeginLatex
//
-// As we have seen, the reading of images is managed by the class \doxygen{otb::ImageFileReader}
+// As we have seen, the reading of images is managed by the class \doxygen{otb}{ImageFileReader}
// while writing is performed by the class
-// \doxygen{otb::ImageFileWriter}. ITK's pipeline implements
+// \doxygen{otb}{ImageFileWriter}. ITK's pipeline implements
// streaming. That means that a filter for which the
// \code{ThreadedGenerateData} method is implemented, will only produce the
// data for the region requested by the following filter in the
// pipeline. Therefore, in order to use the streaming functionnality
// one needs to use a filter at the end of the pipeline which
// requests for adjacent regions of the image to be processed. In
-// ITK, the \doxygen{itk::StreamingImageFilter} class is used for
+// ITK, the \doxygen{itk}{StreamingImageFilter} class is used for
// this purpose. However, ITK does not implement streaming from/to
// files. This means that even if the pipeline has a small memory
// footprint, the images have to be stored in memory at least after
@@ -44,7 +44,7 @@
// this is transparent for the programmer, and if a streaming loop is
// used at the end of the pipeline, the read operation will be
// streamed. For the file writing, the
-// \doxygen{otb::StreamingImageFileWriter} has to be used.
+// \doxygen{otb}{StreamingImageFileWriter} has to be used.
//
// The first step for performing streamed reading and writing is to include the
// following headers.
@@ -109,7 +109,7 @@ int main( int argc, char ** argv )
// Software Guide : BeginLatex
//
// Then, we create one object of each type using the New() method and
- // assigning the result to a \doxygen{SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// \index{otb::ImageFileReader!New()}
// \index{otb::ImageFileWriter!New()}
diff --git a/Examples/Installation/HelloWorld.cxx b/Examples/Installation/HelloWorld.cxx
index 07c8a45345..d3db26d4fd 100644
--- a/Examples/Installation/HelloWorld.cxx
+++ b/Examples/Installation/HelloWorld.cxx
@@ -44,10 +44,10 @@ int main()
//
// This code instantiates an image whose pixels are represented with
// type \code{unsigned short}. The image is then constructed and assigned to a
-// \doxygen{SmartPointer}. Although later in the text we will discuss
+// \doxygen{itk}{SmartPointer}. Although later in the text we will discuss
// \code{SmartPointer}'s in detail, for now think of it as a handle on an
// instance of an object (see section \ref{sec:SmartPointers} for more
-// information). The \doxygen{Image} class will be described in
+// information). The \doxygen{itk}{Image} class will be described in
// Section~\ref{sec:ImageSection}.
//
// Software Guide : EndLatex
diff --git a/Examples/Learning/SVMImageClassificationExample.cxx b/Examples/Learning/SVMImageClassificationExample.cxx
index 8e06630efd..55a8d44fe9 100644
--- a/Examples/Learning/SVMImageClassificationExample.cxx
+++ b/Examples/Learning/SVMImageClassificationExample.cxx
@@ -41,7 +41,7 @@
// Software Guide : BeginLatex
// This example illustrates the use of the
-// \doxygen{otb::SVMClassifier} class for performing SVM
+// \doxygen{otb}{SVMClassifier} class for performing SVM
// classification on images.
// In this example, we will use an SVM model estimated in the example
// of section \ref{sec:LearningWithImages}
@@ -49,9 +49,9 @@
// values only. The images used for this example are shown in
// figure~\ref{fig:SVMROIS}.
// The first thing to do is include the header file for the
-// class. Since the \doxygen{otb::SVMClassifier} takes
-// \doxygen{itk::ListSample}s as input, the class
-// \doxygen{itk::PointSetToListAdaptor} is needed.\\
+// class. Since the \doxygen{otb}{SVMClassifier} takes
+// \doxygen{itk}{ListSample}s as input, the class
+// \doxygen{itk}{PointSetToListAdaptor} is needed.\\
//
//
// Software Guide : EndLatex
@@ -143,7 +143,7 @@ int main(int argc, char* argv[] )
//
// The image has now to be transformed to a sample which
// is compatible with the classification framework. We will use a
-// \doxygen{itk::Statistics::ImageToListAdaptor} for this
+// \doxygen{itk}{Statistics::ImageToListAdaptor} for this
// task. This class is templated over the image type used for
// storing the measures.
//
@@ -342,7 +342,7 @@ int main(int argc, char* argv[] )
//
// Only for visualization purposes, we choose to rescale the image of
// classes before sving it to a file. We will use the
-// \doxygen{itk::RescaleIntensityImageFilter} for this purpose.
+// \doxygen{itk}{RescaleIntensityImageFilter} for this purpose.
//
// Software Guide : EndLatex
diff --git a/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx b/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx
index bc286eeb37..ccd4f21730 100644
--- a/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx
+++ b/Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx
@@ -105,7 +105,7 @@ int main( int argc, char* argv[] )
// Software Guide : BeginLatex
//
-// The \doxygen{otb::SVMImageModelEstimator} class is templated over
+// The \doxygen{otb}{SVMImageModelEstimator} class is templated over
// the input (features) and the training (labels) images.
//
// Software Guide : EndLatex
@@ -214,7 +214,7 @@ int main( int argc, char* argv[] )
//
// The image has now to be transformed to a sample which
// is compatible with the classification framework. We will use a
-// \doxygen{itk::Statistics::ImageToListAdaptor} for this
+// \doxygen{itk}{Statistics::ImageToListAdaptor} for this
// task. This class is templated over the image type used for
// storing the measures.
//
@@ -398,7 +398,7 @@ int main( int argc, char* argv[] )
//
// Only for visualization purposes, we choose to rescale the image of
// classes before sving it to a file. We will use the
-// \doxygen{itk::RescaleIntensityImageFilter} for this purpose.
+// \doxygen{itk}{RescaleIntensityImageFilter} for this purpose.
//
// Software Guide : EndLatex
diff --git a/Examples/Learning/SVMImageModelEstimatorExample.cxx b/Examples/Learning/SVMImageModelEstimatorExample.cxx
index 2babd53e6c..83774a9bb8 100644
--- a/Examples/Learning/SVMImageModelEstimatorExample.cxx
+++ b/Examples/Learning/SVMImageModelEstimatorExample.cxx
@@ -32,7 +32,7 @@
// Software Guide : BeginLatex
// This example illustrates the use of the
-// \doxygen{otb::SVMImageModelEstimator} class. This class allows the
+// \doxygen{otb}{SVMImageModelEstimator} class. This class allows the
// estimation of a SVM model (supervised learning) from a feature
// image and an image of labels. In this example, we will train an SVM
// to separate between water and non-water pixels by using the RGB
@@ -91,7 +91,7 @@ int main( int argc, char* argv[] )
// Software Guide : BeginLatex
//
-// The \doxygen{otb::SVMImageModelEstimator} class is templated over
+// The \doxygen{otb}{SVMImageModelEstimator} class is templated over
// the input (features) and the training (labels) images.
//
// Software Guide : EndLatex
diff --git a/Examples/Learning/SVMPointSetClassificationExample.cxx b/Examples/Learning/SVMPointSetClassificationExample.cxx
index 497fa10df3..cc3cf49cdb 100644
--- a/Examples/Learning/SVMPointSetClassificationExample.cxx
+++ b/Examples/Learning/SVMPointSetClassificationExample.cxx
@@ -36,12 +36,12 @@
// Software Guide : BeginLatex
// This example illustrates the use of the
-// \doxygen{otb::SVMClassifier} class for performing SVM
+// \doxygen{otb}{SVMClassifier} class for performing SVM
// classification on pointsets.
// The first thing to do is include the header file for the
-// class. Since the \doxygen{otb::SVMClassifier} takes
-// \doxygen{itk::ListSample}s as input, the class
-// \doxygen{itk::PointSetToListAdaptor} is needed.\\
+// class. Since the \doxygen{otb}{SVMClassifier} takes
+// \doxygen{itk}{ListSample}s as input, the class
+// \doxygen{itk}{PointSetToListAdaptor} is needed.\\
//
// We start by including the needed header files.
//
@@ -195,7 +195,7 @@ int main( int argc, char* argv[] )
//
// Once the pointset is ready, we must transform it to a sample which
// is compatible with the classification framework. We will use a
-// \doxygen{itk::Statistics::PointSetToListAdaptor} for this
+// \doxygen{itk}{Statistics::PointSetToListAdaptor} for this
// task. This class is templated over the point set type used for
// storing the measures.
//
diff --git a/Examples/Learning/SVMPointSetModelEstimatorExample.cxx b/Examples/Learning/SVMPointSetModelEstimatorExample.cxx
index 4a7d40b4a7..607e8f7ae8 100644
--- a/Examples/Learning/SVMPointSetModelEstimatorExample.cxx
+++ b/Examples/Learning/SVMPointSetModelEstimatorExample.cxx
@@ -32,8 +32,8 @@
// Software Guide : BeginLatex
//
// This example illustrates the use of the
-// \doxygen{otb::otbSVMPointSetModelEstimator} in order to perform the
-// SVM learning from an \doxygen{itk::PointSet} data structure.
+// \doxygen{otb}{otbSVMPointSetModelEstimator} in order to perform the
+// SVM learning from an \doxygen{itk}{PointSet} data structure.
//
// The first step required to use this filter is to include its header file.
//
diff --git a/Examples/Patented/FuzzyConnectednessImageFilter.cxx b/Examples/Patented/FuzzyConnectednessImageFilter.cxx
index e9c0557949..9138a6119b 100644
--- a/Examples/Patented/FuzzyConnectednessImageFilter.cxx
+++ b/Examples/Patented/FuzzyConnectednessImageFilter.cxx
@@ -23,7 +23,7 @@
// Software Guide : BeginLatex
//
// This example illustrates the use of the
-// \doxygen{itk::SimpleFuzzyConnectednessScalarImageFilter}. This filter computes an
+// \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.
@@ -48,7 +48,7 @@
//
// 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
+// \doxygen{itk}{ConfidenceConnectedImageFilter} as a preprocessor that produces a
// rough segmentation and estimates from it the values of the mean and the
// variance.
//
@@ -144,7 +144,7 @@ int main( int argc, char *argv[] )
//
// The fuzzy connectedness segmentation filter is created by invoking the
// \code{New()} method and assigning the result to a
- // \doxygen{itk::SmartPointer}.
+ // \doxygen{itk}{SmartPointer}.
//
// \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!New()}
// \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!Pointer}
diff --git a/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx b/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
index a6c783f062..e5727be692 100644
--- a/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
+++ b/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
@@ -23,19 +23,14 @@
// Software Guide : BeginCommandLineArgs
// INPUTS: {QB_Suburb.png}
// OUTPUTS: {HybridSegmentationFuzzyVoronoiOutput.png}
-// 140 125 140 25 0.2 2.0
-// Software Guide : EndCommandLineArgs
-// Software Guide : BeginCommandLineArgs
-// INPUTS: {QB_Suburb.png}
-// OUTPUTS: {HybridSegmentationFuzzyVoronoiOutput2.png}
-// 80 200 140 300 0.3 3.0
+// 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
+// \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
@@ -93,7 +88,7 @@ int main( int argc, char *argv[] )
// 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
+ // \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
@@ -126,7 +121,7 @@ int main( int argc, char *argv[] )
//
// The fuzzy connectedness segmentation filter is created by invoking the
// \code{New()} method and assigning the result to a
- // \doxygen{itk::SmartPointer}.
+ // \doxygen{itk}{SmartPointer}.
//
// \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!New()}
// \index{itk::SimpleFuzzy\-Connectedness\-Scalar\-Image\-Filter!Pointer}
@@ -191,18 +186,6 @@ int main( int argc, char *argv[] )
//
// Software Guide : EndLatex
- // Software Guide : BeginLatex
- //
- // \begin{figure} \center
- // \includegraphics[width=0.44\textwidth]{QB_Suburb.eps}
- // \includegraphics[width=0.44\textwidth]{HybridSegmentationFuzzyVoronoiOutput2.eps}
- // \itkcaption[Segmentation result for the hybrid segmentation
- // approach]{Another segmentation result for the hybrid segmentation
- // approach.}
- // \label{fig:HybridSegmentationFuzzyVoronoiOutput2}
- // \end{figure}
- //
- // Software Guide : EndLatex
// We instantiate reader and writer types
@@ -351,7 +334,7 @@ int main( int argc, char *argv[] )
// 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}
+ // the gray levels. Here, we add a \doxygen{itk}{RescaleIntensityImageFilter}
// in order to expand the dynamic range to more typical values.
//
// Software Guide : EndLatex
@@ -391,33 +374,19 @@ int main( int argc, char *argv[] )
// \small
// \begin{verbatim}
//HybridSegmentationFuzzyVoronoi QB_Suburb.png Output.png
- // 140 125 140 25 0.2 2.0
+ // 111 38 75 20 0.5 2.0
// \end{verbatim}
// \normalsize
//
- // $(140,125)$ specifies the index position of a seed point in the image,
- // while $140$ and $25$ are the estimated mean and standard deviation,
- // respectively, of the object to be segmented. Finally, $0.2$ and $2.0$
+ // $(111,38)$ 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. For example, when
- // segmenting the input image \code{QB\_Suburb.png} we apply the
- // following new set of parameters parameters.
- //
- // \small
- // \begin{verbatim}
- //HybridSegmentationFuzzyVoronoi QB_Suburb.png Output.png
- // 80 200 140 300 0.3 3.0
- // \end{verbatim}
- // \normalsize
- //
- // Figure~\ref{fig:HybridSegmentationFuzzyVoronoiOutput2} shows the input
- // image and the binary mask resulting from this segmentation. Note that,
- // we can segment color (RGB) and other multi-channel images using an
- // approach similar to this example.
+ // parameters have to be adjusted to reflect the data.
//
// Software Guide : EndLatex
diff --git a/Examples/Segmentation/ConfidenceConnected.cxx b/Examples/Segmentation/ConfidenceConnected.cxx
index 65716c3113..96760f4720 100644
--- a/Examples/Segmentation/ConfidenceConnected.cxx
+++ b/Examples/Segmentation/ConfidenceConnected.cxx
@@ -51,7 +51,7 @@
// \index{itk::ConfidenceConnectedImageFilter!header}
//
// The following example illustrates the use of the
-// \doxygen{itk::ConfidenceConnectedImageFilter}. The criterion used by the
+// \doxygen{itk}{ConfidenceConnectedImageFilter}. The criterion used by the
// ConfidenceConnectedImageFilter is based on simple statistics of the
// current region. First, the algorithm computes the mean and standard
// deviation of intensity values for all the pixels currently included in the
@@ -78,7 +78,7 @@
// considered for inclusion in the region.
//
// Let's look at the minimal code required to use this algorithm. First, the
-// following header defining the \doxygen{itk::ConfidenceConnectedImageFilter} class
+// following header defining the \doxygen{itk}{ConfidenceConnectedImageFilter} class
// must be included.
//
// Software Guide : EndLatex
@@ -98,7 +98,7 @@
// Noise present in the image can reduce the capacity of this filter to grow
// large regions. When faced with noisy images, it is usually convenient to
// pre-process the image by using an edge-preserving smoothing filter. In this particular example we use the
-// \doxygen{itk::CurvatureFlowImageFilter}, hence we need to include its header
+// \doxygen{itk}{CurvatureFlowImageFilter}, hence we need to include its header
// file.
//
// Software Guide : EndLatex
@@ -173,7 +173,7 @@ int main( int argc, char *argv[] )
// Software Guide : BeginLatex
//
// Next the filter is created by invoking the \code{New()} method and
- // assigning the result to a \doxygen{itk::SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// Software Guide : EndLatex
@@ -306,7 +306,7 @@ int main( int argc, char *argv[] )
// \emph{typical} region of the structure to be segmented. A
// small neighborhood around the seed point will be used to compute the
// initial mean and standard deviation for the inclusion criterion. The
- // seed is passed in the form of a \doxygen{itk::Index} to the \code{SetSeed()}
+ // seed is passed in the form of a \doxygen{itk}{Index} to the \code{SetSeed()}
// method.
//
// \index{itk::ConfidenceConnectedImageFilter!SetSeed()}
@@ -381,7 +381,7 @@ int main( int argc, char *argv[] )
// \itkcaption[ConnectedThreshold example parameters]{Parameters used for
// segmenting some structures shown in
// Figure~\ref{fig:ConnectedThresholdOutput} with the filter
- // \doxygen{ConnectedThresholdImageFilter}.\label{tab:ConfidenceConnectedThresholdOutput}}
+ // \doxygen{itk}{ConnectedThresholdImageFilter}.\label{tab:ConfidenceConnectedThresholdOutput}}
// \end{table}
//
//
diff --git a/Examples/Segmentation/ConnectedThresholdImageFilter.cxx b/Examples/Segmentation/ConnectedThresholdImageFilter.cxx
index 808492b413..a7a14a940c 100644
--- a/Examples/Segmentation/ConnectedThresholdImageFilter.cxx
+++ b/Examples/Segmentation/ConnectedThresholdImageFilter.cxx
@@ -50,7 +50,7 @@
// Software Guide : BeginLatex
//
// The following example illustrates the use of the
-// \doxygen{itk::ConnectedThresholdImageFilter}. This filter uses the
+// \doxygen{itk}{ConnectedThresholdImageFilter}. This filter uses the
// flood fill iterator. Most of the algorithmic complexity of a region
// growing method comes from visiting neighboring pixels. The flood
// fill iterator assumes this responsibility and greatly simplifies
@@ -92,7 +92,7 @@
// Noise present in the image can reduce the capacity of this filter to grow
// large regions. When faced with noisy images, it is usually convenient to
// pre-process the image by using an edge-preserving smoothing filter. In this particular example we use the
-// \doxygen{CurvatureFlowImageFilter}, hence we need to include its header
+// \doxygen{itk}{CurvatureFlowImageFilter}, hence we need to include its header
// file.
//
// Software Guide : EndLatex
@@ -167,7 +167,7 @@ int main( int argc, char *argv[])
// Software Guide : BeginLatex
//
// Then the filter is created by invoking the \code{New()} method and
- // assigning the result to a \doxygen{itk::SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// Software Guide : EndLatex
@@ -278,7 +278,7 @@ int main( int argc, char *argv[])
// The initialization of the algorithm requires the user to provide a seed
// point. It is convenient to select this point to be placed in a
// \emph{typical} region of the structure to be segmented. The
- // seed is passed in the form of a \doxygen{itk::Index} to the \code{SetSeed()}
+ // seed is passed in the form of a \doxygen{itk}{Index} to the \code{SetSeed()}
// method.
//
// \index{itk::ConnectedThresholdImageFilter!SetSeed()}
@@ -341,7 +341,7 @@ int main( int argc, char *argv[])
// \itkcaption[ConnectedThreshold example parameters]{Parameters used for
// segmenting some structures shown in
// Figure~\ref{fig:ConnectedThresholdOutput} with the filter
- // \doxygen{ConnectedThresholdImageFilter}.\label{tab:ConnectedThresholdOutput}}
+ // \doxygen{itk}{ConnectedThresholdImageFilter}.\label{tab:ConnectedThresholdOutput}}
// \end{table}
//
// \begin{figure} \center
diff --git a/Examples/Segmentation/FastMarchingImageFilter.cxx b/Examples/Segmentation/FastMarchingImageFilter.cxx
index 3b19fb3f7b..b30b3f9b34 100644
--- a/Examples/Segmentation/FastMarchingImageFilter.cxx
+++ b/Examples/Segmentation/FastMarchingImageFilter.cxx
@@ -53,7 +53,7 @@
// can be used.
//
// The following example illustrates the use of the
-// \doxygen{itk::FastMarchingImageFilter}. This filter implements a fast marching
+// \doxygen{itk}{FastMarchingImageFilter}. This filter implements a fast marching
// solution to a simple level set evolution problem. In this example, the
// speed term used in the differential equation is expected to be provided by
// the user in the form of an image. This image is typically computed as a
@@ -90,12 +90,12 @@
// Figure~\ref{fig:FastMarchingCollaborationDiagram} shows the major components
// involved in the application of the FastMarchingImageFilter to a
// segmentation task. It involves an initial stage of smoothing using the
-// \doxygen{itk::CurvatureAnisotropicDiffusionImageFilter}. The smoothed image is
+// \doxygen{itk}{CurvatureAnisotropicDiffusionImageFilter}. The smoothed image is
// passed as the input to the
-// \doxygen{itk::GradientMagnitudeRecursiveGaussianImageFilter} and then to the
-// \doxygen{itk::SigmoidImageFilter}. Finally, the output of the
+// \doxygen{itk}{GradientMagnitudeRecursiveGaussianImageFilter} and then to the
+// \doxygen{itk}{SigmoidImageFilter}. Finally, the output of the
// FastMarchingImageFilter is passed to a
-// \doxygen{itk::BinaryThresholdImageFilter} in order to produce a binary mask
+// \doxygen{itk}{BinaryThresholdImageFilter} in order to produce a binary mask
// representing the segmented object.
//
// The code in the following example illustrates the typical setup of a
@@ -132,7 +132,7 @@
// Software Guide : BeginLatex
//
-// Of course, we will need the \doxygen{otb::Image} class and the
+// Of course, we will need the \doxygen{otb}{Image} class and the
// FastMarchingImageFilter class. Hence we include their headers.
//
// Software Guide : EndLatex
@@ -158,8 +158,8 @@
// Software Guide : BeginLatex
//
-// Reading and writing images will be done with the \doxygen{otb::ImageFileReader}
-// and \doxygen{otb::ImageFileWriter}.
+// Reading and writing images will be done with the \doxygen{otb}{ImageFileReader}
+// and \doxygen{otb}{ImageFileWriter}.
//
// Software Guide : EndLatex
@@ -169,7 +169,7 @@
// Software Guide : EndCodeSnippet
-// The \doxygen{itk::RescaleIntensityImageFilter} is used to renormailize the
+// The \doxygen{itk}{RescaleIntensityImageFilter} is used to renormailize the
// output of filters before sending them to files.
//
#include "itkRescaleIntensityImageFilter.h"
@@ -291,7 +291,7 @@ int main( int argc, char *argv[] )
// Software Guide : BeginLatex
//
// Then, the filter is created by invoking the \code{New()} method and
- // assigning the result to a \doxygen{itk::SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// Software Guide : EndLatex
@@ -514,7 +514,7 @@ int main( int argc, char *argv[] )
// Software Guide : BeginLatex
//
// Nodes are created as stack variables and initialized with a value and an
- // \doxygen{itk::Index} position.
+ // \doxygen{itk}{Index} position.
//
// \index{itk::FastMarchingImageFilter!Seed initialization}
//
@@ -749,11 +749,11 @@ int main( int argc, char *argv[] )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{itk::ShapeDetectionLevelSetImageFilter}
- // \item \doxygen{itk::GeodesicActiveContourLevelSetImageFilter}
- // \item \doxygen{itk::ThresholdSegmentationLevelSetImageFilter}
- // \item \doxygen{itk::CannySegmentationLevelSetImageFilter}
- // \item \doxygen{itk::LaplacianSegmentationLevelSetImageFilter}
+ // \item \doxygen{itk}{ShapeDetectionLevelSetImageFilter}
+ // \item \doxygen{itk}{GeodesicActiveContourLevelSetImageFilter}
+ // \item \doxygen{itk}{ThresholdSegmentationLevelSetImageFilter}
+ // \item \doxygen{itk}{CannySegmentationLevelSetImageFilter}
+ // \item \doxygen{itk}{LaplacianSegmentationLevelSetImageFilter}
// \end{itemize}
//
// See the ITK Software Guide for examples of the use of these classes.
diff --git a/Examples/Segmentation/IsolatedConnectedImageFilter.cxx b/Examples/Segmentation/IsolatedConnectedImageFilter.cxx
index 9250c74daa..61f28b7a8d 100644
--- a/Examples/Segmentation/IsolatedConnectedImageFilter.cxx
+++ b/Examples/Segmentation/IsolatedConnectedImageFilter.cxx
@@ -40,8 +40,8 @@
// Software Guide : BeginLatex
//
// The following example illustrates the use of the
-// \doxygen{itk::IsolatedConnectedImageFilter}. This filter is a close variant of
-// the \doxygen{itk::ConnectedThresholdImageFilter}. In this filter two seeds and a
+// \doxygen{itk}{IsolatedConnectedImageFilter}. This filter is a close variant of
+// the \doxygen{itk}{ConnectedThresholdImageFilter}. In this filter two seeds and a
// lower threshold are provided by the user. The filter will grow a region
// connected to the first seed and \textbf{not connected} to the second one. In
// order to do this, the filter finds an intensity value that could be used as
@@ -201,7 +201,7 @@ int main( int argc, char *argv[] )
// Software Guide : BeginLatex
//
- // As in the \doxygen{itk::ConnectedThresholdImageFilter} we must now specify
+ // As in the \doxygen{itk}{ConnectedThresholdImageFilter} we must now specify
// the intensity value to be set on the output pixels and at least one
// seed point to define the initial region.
//
@@ -264,7 +264,7 @@ int main( int argc, char *argv[] )
// that of the input image. As a reminder of this fact, Figure
// \ref{fig:IsolatedConnectedImageFilterOutput} presents, from left to
// right, the input image and the result of smoothing with the
- // \doxygen{CurvatureFlowImageFilter} followed by segmentation results.
+ // \doxygen{itk}{CurvatureFlowImageFilter} followed by segmentation results.
//
// This filter is intended to be used in cases where adjacent
// structures are difficult to separate. Selecting one seed in one structure
diff --git a/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx b/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
index 8418a5fdb3..ae5421b682 100644
--- a/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
+++ b/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
@@ -46,8 +46,8 @@
// Software Guide : BeginLatex
//
// The following example illustrates the use of the
-// \doxygen{itk::NeighborhoodConnectedImageFilter}. This filter is a close variant
-// of the \doxygen{itk::ConnectedThresholdImageFilter}. On one hand, the
+// \doxygen{itk}{NeighborhoodConnectedImageFilter}. This filter is a close variant
+// of the \doxygen{itk}{ConnectedThresholdImageFilter}. On one hand, the
// ConnectedThresholdImageFilter accepts a pixel in the region if its intensity
// is in the interval defined by two user-provided threshold values. The
// NeighborhoodConnectedImageFilter, on the other hand, will only accept a
@@ -76,7 +76,7 @@
// Software Guide : BeginLatex
//
-// The \doxygen{itk::CurvatureFlowImageFilter} is used here to smooth the image
+// The \doxygen{itk}{CurvatureFlowImageFilter} is used here to smooth the image
// while preserving edges.
//
// Software Guide : EndLatex
@@ -151,7 +151,7 @@ int main( int argc, char *argv[] )
// Software Guide : BeginLatex
//
// Then, the filter is created by invoking the \code{New()} method and
- // assigning the result to a \doxygen{itk::SmartPointer}.
+ // assigning the result to a \doxygen{itk}{SmartPointer}.
//
// Software Guide : EndLatex
@@ -328,7 +328,7 @@ int main( int argc, char *argv[] )
// \itkcaption[NeighborhoodConnectedThreshold example parameters]{Parameters used for
// segmenting some structures shown in
// Figure~\ref{fig:NeighborhoodConnectedThresholdOutput} with the filter
- // \doxygen{itk::NeighborhoodConnectedThresholdImageFilter}.\label{tab:NeighborhoodConnectedThresholdOutput}}
+ // \doxygen{itk}{NeighborhoodConnectedThresholdImageFilter}.\label{tab:NeighborhoodConnectedThresholdOutput}}
// \end{table}
//
// \begin{figure} \center
diff --git a/Examples/Segmentation/OtsuMultipleThresholdImageFilter.cxx b/Examples/Segmentation/OtsuMultipleThresholdImageFilter.cxx
index 7e0eb76254..8edf2aa75d 100755
--- a/Examples/Segmentation/OtsuMultipleThresholdImageFilter.cxx
+++ b/Examples/Segmentation/OtsuMultipleThresholdImageFilter.cxx
@@ -33,7 +33,7 @@
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
-// This example illustrates how to use the \doxygen{itk::OtsuMultipleThresholdsCalculator}.
+// This example illustrates how to use the \doxygen{itk}{OtsuMultipleThresholdsCalculator}.
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
@@ -205,7 +205,7 @@ int main( int argc, char * argv[] )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{itk::ThresholdImageFilter}
+ // \item \doxygen{itk}{ThresholdImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/Segmentation/OtsuThresholdImageFilter.cxx b/Examples/Segmentation/OtsuThresholdImageFilter.cxx
index f834da1964..60787a9bfc 100755
--- a/Examples/Segmentation/OtsuThresholdImageFilter.cxx
+++ b/Examples/Segmentation/OtsuThresholdImageFilter.cxx
@@ -34,7 +34,7 @@
// Software Guide : BeginLatex
//
-// This example illustrates how to use the \doxygen{itk::OtsuThresholdImageFilter}.
+// This example illustrates how to use the \doxygen{itk}{OtsuThresholdImageFilter}.
//
// Software Guide : EndLatex
@@ -97,7 +97,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileReader} class is also instantiated in order to read
+ // An \doxygen{otb}{ImageFileReader} class is also instantiated in order to read
// image data from a file. (See Section \ref{sec:IO} on page
// \pageref{sec:IO} for more information about reading
// and writing data.)
@@ -111,7 +111,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
- // An \doxygen{otb::ImageFileWriter} is instantiated in order to write the output
+ // An \doxygen{otb}{ImageFileWriter} is instantiated in order to write the output
// image to a file.
//
// Software Guide : EndLatex
@@ -125,7 +125,7 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
//
// Both the filter and the reader are created by invoking their \code{New()}
- // methods and assigning the result to \doxygen{SmartPointer}s.
+ // methods and assigning the result to \doxygen{itk}{SmartPointer}s.
//
// Software Guide : EndLatex
@@ -238,7 +238,7 @@ int main( int argc, char * argv[] )
//
// \relatedClasses
// \begin{itemize}
- // \item \doxygen{itk::ThresholdImageFilter}
+ // \item \doxygen{itk}{ThresholdImageFilter}
// \end{itemize}
//
// Software Guide : EndLatex
diff --git a/Examples/Segmentation/WatershedSegmentation.cxx b/Examples/Segmentation/WatershedSegmentation.cxx
index a9b271cc97..2225c49f4f 100644
--- a/Examples/Segmentation/WatershedSegmentation.cxx
+++ b/Examples/Segmentation/WatershedSegmentation.cxx
@@ -44,7 +44,7 @@
// Software Guide : BeginLatex
//
// The following example illustrates how to preprocess and segment images
-// using the \doxygen{itk::WatershedImageFilter}. Note that the care with which
+// using the \doxygen{itk}{WatershedImageFilter}. Note that the care with which
// the data is preprocessed will greatly affect the quality of your result.
// Typically, the best results are obtained by preprocessing the original
// image with an edge-preserving diffusion filter, such as one of the
@@ -54,9 +54,9 @@
// object boundaries. A suitable height function for many applications can
// be generated as the gradient magnitude of the image to be segmented.
//
-// The \doxygen{itk::VectorGradientMagnitudeAnisotropicDiffusionImageFilter} class
+// The \doxygen{itk}{VectorGradientMagnitudeAnisotropicDiffusionImageFilter} class
// is used to smooth the image and the
-// \doxygen{itk::VectorGradientMagnitudeImageFilter} is used to generate the
+// \doxygen{itk}{VectorGradientMagnitudeImageFilter} is used to generate the
// height function. We begin by including all preprocessing filter header
// files and the header file for the WatershedImageFilter. We
// use the vector versions of these filters because the input data is a color
@@ -96,10 +96,10 @@ int main( int argc, char *argv[] )
// work properly. The preprocessing stages are done directly on the
// vector-valued data and the segmentation is done using floating point
// scalar data. Images are converted from RGB pixel type to
- // numerical vector type using \doxygen{VectorCastImageFilter}.
+ // numerical vector type using \doxygen{itk}{VectorCastImageFilter}.
// Please pay attention to the fact that we are using
- // \doxygen{itk::Image}s since the
- // \doxygen{itk::VectorGradientMagnitudeImageFilter} has some
+ // \doxygen{itk}{Image}s since the
+ // \doxygen{itk}{VectorGradientMagnitudeImageFilter} has some
// internal typedefs which make polymorfism impossible.
// Software Guide : EndLatex
@@ -196,10 +196,10 @@ int main( int argc, char *argv[] )
// for the purposes of this example, we will convert it to RGB pixels. RGB
// images have the advantage that they can be saved as a simple png file
// and viewed using any standard image viewer software. The
- // \subdoxygen{itk::Functor}{ScalarToRGBPixelFunctor} class is a special
+ // \subdoxygen{itk}{Functor}{ScalarToRGBPixelFunctor} class is a special
// function object designed to hash a scalar value into an
- // \doxygen{itk::RGBPixel}. Plugging this functor into the
- // \doxygen{itk::UnaryFunctorImageFilter} creates an image filter for that
+ // \doxygen{itk}{RGBPixel}. Plugging this functor into the
+ // \doxygen{itk}{UnaryFunctorImageFilter} creates an image filter for that
// converts scalar images to RGB images.
//
// Software Guide : EndLatex
diff --git a/Examples/Visu/GreyVisuExample.cxx b/Examples/Visu/GreyVisuExample.cxx
index 503d0733e2..67b3904adb 100644
--- a/Examples/Visu/GreyVisuExample.cxx
+++ b/Examples/Visu/GreyVisuExample.cxx
@@ -26,7 +26,7 @@
// Software Guide : BeginLatex
//
// \textbf{FIXME !! This example segfaults (see bug no. 27)}.
- // This example shows the use of the \doxygen{otb::ImageViewer}
+ // This example shows the use of the \doxygen{otb}{ImageViewer}
// class for greylevel image visualization. As usual, we start by
// including the header file for the class.
//
@@ -124,7 +124,7 @@ int main( int argc, char ** argv )
// mevel image visualization.}
// \label{fig:VisuGrey}
// \end{figure}
- // The the \doxygen{otb::ImageViewer} class creates 3 windows (see
+ // The the \doxygen{otb}{ImageViewer} class creates 3 windows (see
// figure \ref{fig:VisuGrey}) for an improved visualization of large
// images. This procedure is inspired from the navigation window of
// the Gimp and other image visualization tools. The navigation
diff --git a/Examples/Visu/VisuExample1.cxx b/Examples/Visu/VisuExample1.cxx
index 06826c6250..66916081ca 100644
--- a/Examples/Visu/VisuExample1.cxx
+++ b/Examples/Visu/VisuExample1.cxx
@@ -25,7 +25,7 @@
// Software Guide : BeginLatex
//
- // This example shows the use of the \doxygen{otb::ImageViewer}
+ // This example shows the use of the \doxygen{otb}{ImageViewer}
// class for image visualization. As usual, we start by
// including the header file for the class.
//
@@ -122,7 +122,7 @@ int main( int argc, char ** argv )
// \itkcaption[Image visualization.]{Example of image visualization.}
// \label{fig:Visu1}
// \end{figure}
- // The the \doxygen{otb::ImageViewer} class creates 3 windows (see
+ // The the \doxygen{otb}{ImageViewer} class creates 3 windows (see
// figure \ref{fig:Visu1}) for an improved visualization of large
// images. This procedure is inspired from the navigation window of
// the Gimp and other image visualization tools. The navigation
--
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