From 8c5b394bb0754ffd8248615eb09c29a5d205633e Mon Sep 17 00:00:00 2001
From: Jordi Inglada <jordi.inglada@orfeo-toolbox.org>
Date: Thu, 29 Jun 2006 13:42:38 +0000
Subject: [PATCH] Mise a jour exemples pour doc
---
.../ChangeDetectionFrameworkExample.cxx | 3 +--
Examples/DataRepresentation/Image/Image3.cxx | 8 ++++----
.../AssymmetricFusionOfLineDetectorExample.cxx | 4 ++--
Examples/Patented/CMakeLists.txt | 8 ++++----
Examples/Segmentation/ConfidenceConnected.cxx | 6 ++----
.../Segmentation/ConnectedThresholdImageFilter.cxx | 4 +---
Examples/Segmentation/FastMarchingImageFilter.cxx | 14 +++++++-------
.../NeighborhoodConnectedImageFilter.cxx | 2 +-
8 files changed, 22 insertions(+), 27 deletions(-)
diff --git a/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx b/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
index 0e3471701e..1b2de12149 100644
--- a/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
+++ b/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
@@ -62,8 +62,7 @@
//
// Since the change detectors operate on neighborhoods, the functor
// call will take 2 arguments which are
-// \doxygen{itk::ConstNeighborhoodIterator}s. See chapter
-// \ref{sec:ImageIteratorsChapter} for more information about ITK iterators.
+// \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
diff --git a/Examples/DataRepresentation/Image/Image3.cxx b/Examples/DataRepresentation/Image/Image3.cxx
index 2f5cc610f6..e375cd1c16 100644
--- a/Examples/DataRepresentation/Image/Image3.cxx
+++ b/Examples/DataRepresentation/Image/Image3.cxx
@@ -29,10 +29,10 @@
// pixel data contained in the image. Note that these two methods are
// relatively slow and should not be used in situations where
// high-performance access is required. Image iterators are the appropriate
-// mechanism to efficiently access image pixel data. (See
-// Chapter~\ref{sec:ImageIteratorsChapter} on page
-// \pageref{sec:ImageIteratorsChapter} for information about image
-// iterators.)
+// mechanism to efficiently access image pixel data. %(See
+// %Chapter~\ref{sec:ImageIteratorsChapter} on page
+// %\pageref{sec:ImageIteratorsChapter} for information about image
+// %iterators.)
//
// Software Guide : EndLatex
diff --git a/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx b/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx
index baec52635e..6fe53c8a52 100644
--- a/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx
+++ b/Examples/FeatureExtraction/AssymmetricFusionOfLineDetectorExample.cxx
@@ -227,14 +227,14 @@ int main( int argc, char * argv[] )
// Software Guide : BeginLatex
- // Figure~\ref{fig:LINECORRELATION_FILTER}
+ // Figure~\ref{fig:LINEFUSION_FILTER}
// shows the result of applying the AssymetricFusionOf edge detector filter
// to a SAR image. \begin{figure} \center
// \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
- // image. From left to right : original image, line intensity.} \label{fig:LINECORRELATION_FILTER} \end{figure}
+ // image. From left to right : original image, line intensity.} \label{fig:LINEFUSION_FILTER} \end{figure}
//
// Software Guide : EndLatex
diff --git a/Examples/Patented/CMakeLists.txt b/Examples/Patented/CMakeLists.txt
index d12b8cbf6d..d35c93b5e2 100644
--- a/Examples/Patented/CMakeLists.txt
+++ b/Examples/Patented/CMakeLists.txt
@@ -2,10 +2,10 @@ PROJECT(PatentedExamples)
INCLUDE_REGULAR_EXPRESSION("^.*$")
-ADD_EXECUTABLE(FuzzyConnectednessImageFilter FuzzyConnectednessImageFilter.cxx )
-TARGET_LINK_LIBRARIES(FuzzyConnectednessImageFilter OTBIO OTBCommon ITKNumerics ITKIO)
+#ADD_EXECUTABLE(FuzzyConnectednessImageFilter FuzzyConnectednessImageFilter.cxx )
+#TARGET_LINK_LIBRARIES(FuzzyConnectednessImageFilter OTBIO OTBCommon ITKNumerics ITKIO)
-ADD_EXECUTABLE(HybridSegmentationFuzzyVoronoi HybridSegmentationFuzzyVoronoi.cxx )
-TARGET_LINK_LIBRARIES(HybridSegmentationFuzzyVoronoi OTBIO OTBCommon ITKNumerics ITKIO)
+#ADD_EXECUTABLE(HybridSegmentationFuzzyVoronoi HybridSegmentationFuzzyVoronoi.cxx )
+#TARGET_LINK_LIBRARIES(HybridSegmentationFuzzyVoronoi OTBIO OTBCommon ITKNumerics ITKIO)
diff --git a/Examples/Segmentation/ConfidenceConnected.cxx b/Examples/Segmentation/ConfidenceConnected.cxx
index c823dc855d..65716c3113 100644
--- a/Examples/Segmentation/ConfidenceConnected.cxx
+++ b/Examples/Segmentation/ConfidenceConnected.cxx
@@ -97,9 +97,7 @@
//
// 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. Any of
-// the filters discussed in Section~\ref{sec:EdgePreservingSmoothingFilters}
-// can be used to this end. In this particular example we use the
+// 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
// file.
//
@@ -383,7 +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:ConnectedThresholdOutput}}
+ // \doxygen{ConnectedThresholdImageFilter}.\label{tab:ConfidenceConnectedThresholdOutput}}
// \end{table}
//
//
diff --git a/Examples/Segmentation/ConnectedThresholdImageFilter.cxx b/Examples/Segmentation/ConnectedThresholdImageFilter.cxx
index 642e8dab4d..808492b413 100644
--- a/Examples/Segmentation/ConnectedThresholdImageFilter.cxx
+++ b/Examples/Segmentation/ConnectedThresholdImageFilter.cxx
@@ -91,9 +91,7 @@
//
// 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. Any of
-// the filters discussed in Section~\ref{sec:EdgePreservingSmoothingFilters}
-// could be used to this end. In this particular example we use the
+// 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
// file.
//
diff --git a/Examples/Segmentation/FastMarchingImageFilter.cxx b/Examples/Segmentation/FastMarchingImageFilter.cxx
index 7d90e5a38d..3b19fb3f7b 100644
--- a/Examples/Segmentation/FastMarchingImageFilter.cxx
+++ b/Examples/Segmentation/FastMarchingImageFilter.cxx
@@ -339,9 +339,9 @@ int main( int argc, char *argv[] )
// defined with the methods \code{SetOutputMinimum()} and
// \code{SetOutputMaximum()}. In our case, we want these two values to be
// $0.0$ and $1.0$ respectively in order to get a nice speed image to feed
- // to the FastMarchingImageFilter. Additional details on the use of
- // the SigmoidImageFilter are presented in
- // Section~\ref{sec:IntensityNonLinearMapping}.
+ // to the FastMarchingImageFilter. %Additional details on the use of
+ // %the SigmoidImageFilter are presented in
+ // %Section~\ref{sec:IntensityNonLinearMapping}.
//
// Software Guide : EndLatex
@@ -397,8 +397,8 @@ int main( int argc, char *argv[] )
//
// The CurvatureAnisotropicDiffusionImageFilter class requires a couple
// of parameters to be defined. The following are typical values. However they may have to be adjusted depending on the amount of
- // noise present in the input image. This filter has been discussed in
- // Section~\ref{sec:GradientAnisotropicDiffusionImageFilter}.
+ // noise present in the input image. %This filter has been discussed in
+ // %Section~\ref{sec:GradientAnisotropicDiffusionImageFilter}.
//
// Software Guide : EndLatex
@@ -414,8 +414,8 @@ int main( int argc, char *argv[] )
// The GradientMagnitudeRecursiveGaussianImageFilter performs the
// equivalent of a convolution with a Gaussian kernel followed by a
// derivative operator. The sigma of this Gaussian can be used to control
- // the range of influence of the image edges. This filter has been discussed
- // in Section~\ref{sec:GradientMagnitudeRecursiveGaussianImageFilter}
+ // the range of influence of the image edges. %This filter has been discussed
+ // %in Section~\ref{sec:GradientMagnitudeRecursiveGaussianImageFilter}
//
// \index{itk::Gradient\-Magnitude\-Recursive\-Gaussian\-Image\-Filter!SetSigma()}
//
diff --git a/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx b/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
index 031595df15..8418a5fdb3 100644
--- a/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
+++ b/Examples/Segmentation/NeighborhoodConnectedImageFilter.cxx
@@ -344,7 +344,7 @@ int main( int argc, char *argv[] )
// As with the ConnectedThresholdImageFilter, several seeds could
// be provided to the filter by using the \code{AddSeed()} method.
// Compare the output of Figure
- // \ref{fig:NeighborhoodConnectedImageFilterOutput} with those of Figure
+ // \ref{fig:NeighborhoodConnectedThresholdOutput} with those of Figure
// \ref{fig:ConnectedThresholdOutput} produced by the
// ConnectedThresholdImageFilter. You may want to play with the
// value of the neighborhood radius and see how it affect the smoothness of
--
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