STYLE: fix various spelling errors using codespell

CMake/Description.txt

-Remote sensing processing library developped by CNES ORFEO Toolbox (OTB) is
+Remote sensing processing library developed by CNES ORFEO Toolbox (OTB) is
   distributed as an open source library of image processing algorithms. OTB is
   based on the medical image processing library ITK and offers particular
   functionalities for remote sensing image processing in general and for high
@@ -10,4 +10,4 @@ Remote sensing processing library developped by CNES ORFEO Toolbox (OTB) is
   possible.
   .
   This package provide the new version of the Monteverdi GUI application
-  developped in Qt around the OTB library.
+  developed in Qt around the OTB library.

CMake/FindGBenchmark.cmake

@@ -11,7 +11,7 @@ endif ()
 
 if (NOT GBENCHMARK_INCLUDE_DIR)
   find_path(GBENCHMARK_INCLUDE_DIR NAMES benchmark.h PATH_SUFFIXES benchmark)
-  set(GBENCHMARK_INCLUDE_DIR ${GBENCHMARK_INCLUDE_DIR}/benchmark CACHE PATH "Google.benchmark include direcory")
+  set(GBENCHMARK_INCLUDE_DIR ${GBENCHMARK_INCLUDE_DIR}/benchmark CACHE PATH "Google.benchmark include directory")
 endif ()
 
 mark_as_advanced(GBENCHMARK_INCLUDE_DIR)

CMake/FindLibSVM.cmake

@@ -9,7 +9,7 @@
 #  LIBSVM_INCLUDE_DIR - where to find svm.h
 #  LIBSVM_INCLUDE_DIRS - libsvm includes
 #  LIBSVM_LIBRARY - where to find the LibSVM library
-#  LIBSVM_LIBRARIES - aditional libraries
+#  LIBSVM_LIBRARIES - additional libraries
 #  LIBSVM_MAJOR_VERSION - major version
 #  LIBSVM_MINOR_VERSION - minor version
 #  LIBSVM_PATCH_VERSION - patch version

CMake/FindQwt.cmake

@@ -6,7 +6,7 @@
 #  QWT_INCLUDE_DIR - where to find qwt_plot.h
 #  QWT_INCLUDE_DIRS - qwt includes
 #  QWT_LIBRARY - where to find the Qwt library
-#  QWT_LIBRARIES - aditional libraries
+#  QWT_LIBRARIES - additional libraries
 #  QWT_MAJOR_VERSION - major version
 #  QWT_MINOR_VERSION - minor version
 #  QWT_PATCH_VERSION - patch version

CMake/OTBSetStandardCompilerFlags.cmake

@@ -99,7 +99,7 @@ function(check_compiler_warning_flags c_warning_flags_var cxx_warning_flags_var)
       #-wd1419 #Needed for Intel compilers with remark  #1419: external declaration in primary source file
       #-wd1572 #Needed for Intel compilers with remark  #1572: floating-point equality and inequality comparisons are unreliable
       #-wd2259 #Needed for Intel compilers with remark  #2259: non-pointer conversion from "otb::SizeValueType={unsigned long}" to "double" may lose significant bits
-      #-wd1268 #Needed for Intel compliers with warning #1268: support for exported templates is disabled
+      #-wd1268 #Needed for Intel compilers with warning #1268: support for exported templates is disabled
     else()
       set(VerboseWarningsFlag -Wall )
     endif ()
@@ -149,7 +149,7 @@ macro(check_compiler_platform_flags)
   if(MSVC)
     if (${CMAKE_VERSION} VERSION_GREATER "2.8.10.2")
       if("${CMAKE_EXE_LINKER_FLAGS}" MATCHES "/STACK:[0-9]+")
-          message(STATUS "The size of the stack is already defined, so we dont't modified it.")
+          message(STATUS "The size of the stack is already defined, so we don't modified it.")
       else()
           set(OTB_REQUIRED_LINK_FLAGS "${OTB_REQUIRED_LINK_FLAGS} /STACK:10000000")
           message(STATUS "The stack size is set to 10 Mbytes (/STACK:10000000).")

CMake/UseJava.cmake

@@ -120,7 +120,7 @@
 #
 #   Example:
 #   create_javadoc(my_example_doc
-#     PACKAGES com.exmaple.foo com.example.bar
+#     PACKAGES com.example.foo com.example.bar
 #     SOURCEPATH ${CMAKE_CURRENT_SOURCE_PATH}
 #     CLASSPATH ${CMAKE_JAVA_INCLUDE_PATH}
 #     WINDOWTITLE "My example"

Documentation/SoftwareGuide/CMakeLists.txt

@@ -66,7 +66,7 @@ IF( NOT OTB_DATA_LARGEINPUT_ROOT )
 ENDIF( NOT OTB_DATA_LARGEINPUT_ROOT )
 
 # OTB_DATA_PATHS is searched recursively.. you need not enter sub-directories
-SET(OTB_DATA_PATHS "${OTB_DATA_ROOT}/Examples::${OTB_DATA_ROOT}/Input::${OTB_DATA_LARGEINPUT_ROOT}" CACHE STRING "Where the OTB data is. Enter a double colon seperated list.")
+SET(OTB_DATA_PATHS "${OTB_DATA_ROOT}/Examples::${OTB_DATA_ROOT}/Input::${OTB_DATA_LARGEINPUT_ROOT}" CACHE STRING "Where the OTB data is. Enter a double colon separated list.")
 
 #
 # Rebuild the Software Guide figures or not ?

Documentation/SoftwareGuide/ParseCxxExamples.pl

@@ -59,7 +59,7 @@ sub ParseCxxFile {
 
 
 
-  # The following message is a warning writen on the generated .tex
+  # The following message is a warning written on the generated .tex
   # files for preventing them from being manualy edited.
   print OUTFILE "\% Please do NOT edit this file.\n";
   print OUTFILE "\% It has been automatically generated\n";

Documentation/SoftwareGuide/StripCxxExamples.pl

@@ -55,7 +55,7 @@ sub ParseCxxFile {
 
 
 
-  # The following message is a warning writen on the generated .tex
+  # The following message is a warning written on the generated .tex
   # files for preventing them from being manualy edited.
   print OUTFILE "\/\/ Please do NOT edit this file.\n";
   print OUTFILE "\/\/ It has been automatically generated\n";

Examples/BasicFilters/BandMathXImageFilterExample.cxx

@@ -139,7 +139,7 @@ int main( int argc, char* argv[])
 //  Software Guide : BeginLatex
 //
 //  Now, we can define the expression. The variable im1 represents a pixel (made of 4 components) of the input image.
-//  The variable im1b1N5x5 represents a neigborhood of size 5x5 around this pixel (and so on for each band).
+//  The variable im1b1N5x5 represents a neighborhood of size 5x5 around this pixel (and so on for each band).
 //  The last element we need is the operator 'mean'. By setting its inputs with four neigborhoods, we tell this operator to process the four related bands.
 //  As output, it will produce a vector of four components; this is consistent with the fact that we wish to perform a difference with im1.
 //
@@ -154,7 +154,7 @@ int main( int argc, char* argv[])
 
 //  Software Guide : BeginLatex
 //
-//  Note that the importance of the averaging is driven by the names of the neigborhood variables.
+//  Note that the importance of the averaging is driven by the names of the neighborhood variables.
 //  Last thing we have to do, is to set the pipeline:
 //
 //  Software Guide : EndLatex

Examples/BasicFilters/PrintableImageFilterExample.cxx

@@ -51,7 +51,7 @@
 //
 // The band order in the image products can be also quite tricky. It could be in the wavelength order,
 // as it is the case for Quickbird (1: Blue, 2: Green, 3: Red, 4: NIR), in this case, you
-// have to be carefull to reverse the order if you want a natural display. It could also be reverse
+// have to be careful to reverse the order if you want a natural display. It could also be reverse
 // to facilitate direct viewing, as for SPOT5 (1: NIR, 2: Red, 3: Green, 4: SWIR) but in this situations
 // you have to be careful when you process the image.
 //

Examples/ChangeDetection/KullbackLeiblerProfileChDet.cxx

@@ -59,7 +59,7 @@ int main(int argc, char * argv[])
     if (argc != 9)
       {
       std::cerr <<
-      "Detection de changements par mesure de Kullback-Leibler, optimisee par un developpement de Edgeworth\n";
+      "Detection de changements par mesure de Kullback-Leibler, optimisee par un development de Edgeworth\n";
       std::cerr << argv[0] <<
       " imgAv imgAp imgResu winSizeMin winSizeMax outRedIndex outGreenIndex outBlueIndex\n";
       return 1;

Examples/Classification/KMeansImageClassificationExample.cxx

@@ -54,7 +54,7 @@ int main(int itkNotUsed(argc), char * argv[])
 // Software Guide : EndCodeSnippet
 // Software Guide : BeginLatex
 //
-// Our classifier will be genric enough to be able to process images
+// Our classifier will be generic enough to be able to process images
 // with any number of bands. We read the images as
 // \doxygen{otb}{VectorImage}s. The labeled image will be a scalar image.
 //

Examples/Classification/SOMImageClassificationExample.cxx

@@ -57,7 +57,7 @@ int main(int itkNotUsed(argc), char * argv[])
 // Software Guide : EndCodeSnippet
 // Software Guide : BeginLatex
 //
-// Our classifier will be genric enough to be able to process images
+// Our classifier will be generic enough to be able to process images
 // with any number of bands. We read the images as
 // \doxygen{otb}{VectorImage}s. The labeled image will be a scalar image.
 //

Examples/DimensionReduction/ICAExample.cxx

@@ -203,7 +203,7 @@ int main(int itkNotUsed(argc), char* argv[])
   // \itkcaption[PCA Filter (forward trasnformation)]{Result of applying the
   // \doxygen{otb}{FastICAImageFilter} to an image. From left
   // to right:
-  // original image, color composition with first three independant
+  // original image, color composition with first three independent
   // components and output of the
   // inverse mode (the input RGB image).}
   // \label{fig:FastICA_FILTER}

Examples/DisparityMap/NCCRegistrationFilterExample.cxx

@@ -120,7 +120,7 @@ int main(int argc, char** argv)
 
   // Software Guide : BeginLatex
   //
-  // Now, we need to instanciate the NCCRegistrationFilter which is going to perform the registration:
+  // Now, we need to instantiate the NCCRegistrationFilter which is going to perform the registration:
   //
   // Software Guide : EndLatex
 

Examples/DisparityMap/SimpleDisparityMapEstimationExample.cxx

@@ -110,7 +110,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   //
   // Then we define the metric we will use to evaluate the local registration between the fixed and
-  // the moving image. In this example we choosed the \doxygen{itk}{NormalizedCorrelationImageToImageMetric}.
+  // the moving image. In this example we chose the \doxygen{itk}{NormalizedCorrelationImageToImageMetric}.
   //
   // Software Guide : EndLatex
 
@@ -122,7 +122,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   //
   // Disparity map estimation implies evaluation of the moving image at non-grid position. Therefore, an
-  // interpolator is needed. In this example we choosed the \doxygen{itk}{WindowedSincInterpolateImageFunction}.
+  // interpolator is needed. In this example we chose the \doxygen{itk}{WindowedSincInterpolateImageFunction}.
   //
   // Software Guide : EndLatex
 
@@ -137,7 +137,7 @@ int main(int argc, char* argv[])
 
   // Software Guide : BeginLatex
   //
-  // To perform local registration, an optimizer is needed. In this example we choosed the
+  // To perform local registration, an optimizer is needed. In this example we chose the
   // \doxygen{itk}{GradientDescentOptimizer}.
   //
   // Software Guide : EndLatex
@@ -345,7 +345,7 @@ int main(int argc, char* argv[])
 
   // Software Guide : BeginLatex
   //
-  // The disparity map estimation filter is instanciated.
+  // The disparity map estimation filter is instantiated.
   //
   // Software Guide : EndLatex
 
@@ -379,7 +379,7 @@ int main(int argc, char* argv[])
   // Software Guide : BeginLatex
   //
   // The local registration process can lead to wrong deformation values and transform parameters. To Select only
-  // points in point set for which the registration process was succesful, one can set a threshold on the final metric
+  // points in point set for which the registration process was successful, one can set a threshold on the final metric
   // value : points for which the absolute final metric value is below this threshold will be discarded. This
   // threshold can be set with the \code{SetMetricThreshold()} method.
   //

Examples/FeatureExtraction/ComplexMomentsImageFunctionExample.cxx

@@ -80,7 +80,7 @@ int main(int argc, char * argv[])
 
   //  Software Guide : BeginLatex
   //
-  // Next, we plug the input image into the complex moment fucntion
+  // Next, we plug the input image into the complex moment function
   // and we set its parameters.
   //
   //  Software Guide : EndLatex

Examples/FeatureExtraction/EdgeDensityExample.cxx

@@ -97,7 +97,7 @@ int main(int itkNotUsed(argc), char* argv[])
   // We define now the type for the function which will be used by the
   // edge density filter to estimate this density. Here we choose a
   // function which counts the number of non null pixels per area. The
-  // fucntion takes as template the type of the image to be processed.
+  // function takes as template the type of the image to be processed.
   //
   // Software Guide : EndLatex
 
@@ -120,7 +120,7 @@ int main(int itkNotUsed(argc), char* argv[])
   //
   // Finally, we can define the type for the edge density filter which
   // takes as template the input and output image types, the edge
-  // detector type, and the count fucntion type..
+  // detector type, and the count function type..
   //
   // Software Guide : EndLatex
 

Examples/FeatureExtraction/ExtractRoadExample.cxx

@@ -238,7 +238,7 @@ int main(int argc, char * argv[])
   // Software Guide : BeginLatex
   //
   // Roads are not likely to have sharp turns. Therefore we set the max angle parameter,
-  // as well as the link angular threshold. The value is typicaly $\frac{\pi}{8}$.
+  // as well as the link angular threshold. The value is typically $\frac{\pi}{8}$.
   //
   // Software Guide : EndLatex
 

Examples/FeatureExtraction/LineSegmentDetectorExample.cxx

@@ -98,7 +98,7 @@ int main(int argc, char * argv[])
   // detected segments on top of the input image. For this matter, we
   // will use a \doxygen{otb}{VectorDataToMapFilter} which
   // is templated over the input vector data type and the output image
-  // type, and a conbination of a \doxygen{itk}{binaryFunctorImageFilter}
+  // type, and a combination of a \doxygen{itk}{binaryFunctorImageFilter}
   // and the \doxygen{otb}{Functor}{AlphaBlendingFunctor}.
   //
   // Software Guide : EndLatex

Examples/FeatureExtraction/RightAngleDetectionExample.cxx

@@ -155,7 +155,7 @@ int main(int argc, char * argv[])
   // image. For this matter, we will use a
   // \doxygen{otb}{VectorDataToMapFilter}  which is templated over
   // the  input vector data type and the output image type, and a
-  // conbination of a \doxygen{itk}{binaryFunctorImageFilter}
+  // combination of a \doxygen{itk}{binaryFunctorImageFilter}
   // and the \doxygen{otb}{Functor}{UnaryFunctorImageFilter}.
   //
   // Software Guide : EndLatex

Examples/FeatureExtraction/SFSExample.cxx

@@ -33,7 +33,7 @@
 //
 // This example illustrates the use of the
 // \doxygen{otb}{SFSTexturesImageFilter}.
-// This filter computes the Structural Feature Set as descibed in
+// This filter computes the Structural Feature Set as described in
 // \cite{SFS}. These features are textural parameters which give
 // information about the structure of lines passing through each pixel
 // of the image.

Examples/FeatureExtraction/SeamCarvingExample.cxx

@@ -82,7 +82,7 @@ int main(int itkNotUsed(argc), char * argv[])
   //  Software Guide : BeginLatex
   //
   // Energy is computed according to the gradient of the image, thus an
-  // \doxygen{itk}{GradientMagnitudeImageFilter} is instanciated
+  // \doxygen{itk}{GradientMagnitudeImageFilter} is instantiated
   //
   //  Software Guide : EndLatex
 
@@ -134,7 +134,7 @@ int main(int itkNotUsed(argc), char * argv[])
 
   //  Software Guide : BeginLatex
   //
-  // Now that all elements have been instanciated, we start to plug the pipeline
+  // Now that all elements have been instantiated, we start to plug the pipeline
   // and to define the loop.
   //
   //  Software Guide : EndLatex

Examples/FeatureExtraction/SeamCarvingOtherExample.cxx

@@ -95,7 +95,7 @@ int main(int itkNotUsed(argc), char * argv[])
 
   // Software Guide : BeginLatex
   //
-  // We instanciate the different filters of the pipeline as before.
+  // We instantiate the different filters of the pipeline as before.
   //
   // Software Guide : EndLatex
 

Examples/FeatureExtraction/ThresholdToPointSetExample.cxx

@@ -78,7 +78,7 @@ int main(int argc, char * argv[])
 
   //  Software Guide : BeginLatex
   //
-  // A reader is instanciated to read the input image
+  // A reader is instantiated to read the input image
   //
   //  Software Guide : EndLatex
 
@@ -124,7 +124,7 @@ int main(int argc, char * argv[])
   //  Software Guide : BeginLatex
   //
   // To manipulate and display the result of this filter, we manually
-  // instanciate a point set and we call the \code{Update()} method on the
+  // instantiate a point set and we call the \code{Update()} method on the
   // threshold filter to trigger the pipeline execution.
   //
   // After this step, the \code{pointSet} variable contains the point set.

Examples/FeatureExtraction/TouziEdgeDetectorExample.cxx

@@ -27,7 +27,7 @@
 //
 // 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
+// edge detectors but it is appropriate for SAR images, where the
 // speckle noise is considered as multiplicative. By analogy with the
 // classical gradient-based edge detectors which are suited to the
 // additive noise case, this filter computes a ratio of local means in

Examples/Filtering/CannyEdgeDetectionImageFilter.cxx

@@ -83,7 +83,7 @@ int main(int argc, char* argv[])
 
   //  Software Guide : BeginLatex
   //
-  // As the Canny filter works with real values, we can instanciated the reader using
+  // As the Canny filter works with real values, we can instantiated the reader using
   // an image with pixels as double. This does not imply anything on the real image
   // coding format which will be cast into double.
   //

Examples/IO/DEMHandlerExample.cxx

@@ -27,8 +27,8 @@
 // OTB relies on OSSIM for elevation handling. Since release 3.16, there is a
 // single configuration class \doxygen{otb}{DEMHandler} to manage elevation (in
 // image projections or localization functions for example).  This configuration
-// is managed by the a proper instanciation and parameters setting of this
-// class.  These instanciations must be done before any call to geometric
+// is managed by the a proper instantiation and parameters setting of this
+// class.  These instantiations must be done before any call to geometric
 // filters or functionalities. Ossim internal accesses to elevation are also
 // configured by this class and this will ensure consistency throughout the
 // library.

Examples/IO/HDFReaderExample.cxx

@@ -55,7 +55,7 @@ int main(int itkNotUsed(argc), char * argv[])
 
 // Software Guide : BeginLatex
 //
-// We need now to declare the data types that we will be using and instanciate the
+// We need now to declare the data types that we will be using and instantiate the
 // reader (which is a \doxygen{otb}{PointSetFileReader}).
 //
 // Software Guide : EndLatex
@@ -90,7 +90,7 @@ int main(int itkNotUsed(argc), char * argv[])
 // You can access to subdatasets' information available in the HDF file using
 // the \code{GetSubDatasetInfo} method of \doxygen{otb}{GDALImageIO}.
 // It allows storing HDF subdatasets names and descriptions in vector of string.
-// You can find below how to print the name and the decription
+// You can find below how to print the name and the description
 // of all the subdatasets.
 //
 // Software Guide : EndLatex

Examples/IO/ImageToKmzAndMapFileProductExample.cxx

@@ -109,7 +109,7 @@ int main(int argc, char* argv[])
 //
 // Here, we set all the Ground Control Points associated to the image
 // indexes. This is the entry of the rpc sensor model
-// estimator. Everytime a GCP is added, the output image information
+// estimator. Every time a GCP is added, the output image information
 // or its keywordlist is updated. In general, a dozen of GCPs are
 // needed to estimate an accurate sensor model. The points are added
 // via the method AddGCP(PointType2D, PointType3D). The outpput image
@@ -154,7 +154,7 @@ int main(int argc, char* argv[])
 
 // Software Guide : BeginLatex
 //
-// Finally, we trigger the kmz writting by calling the \code{Update()}
+// Finally, we trigger the kmz writing by calling the \code{Update()}
 // method on the writer.
 //
 // Software Guide : EndLatex

Examples/IO/StreamingImageReadWrite.cxx

@@ -25,7 +25,7 @@
 //  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
+//  pipeline. Therefore, in order to use the streaming functionality
 //  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

Examples/IO/TileMapImageIOExample.cxx

@@ -235,7 +235,7 @@ int main(int argc, char* argv[])
   // \center
   // \includegraphics[width=0.45\textwidth]{openStreetMap-Toulouse.eps}
   // \includegraphics[width=0.45\textwidth]{openStreetMap-Singapore.eps}
-  // \itkcaption[Open street map]{Map created from open street map showing the OTB headquaters}
+  // \itkcaption[Open street map]{Map created from open street map showing the OTB headquarters}
   // \label{fig:TILEMAPIMAGEIOEXAMPLE}
   // \end{figure}
   //

Examples/Learning/SEMModelEstimatorExample.cxx

@@ -29,7 +29,7 @@
 // In this example, we present OTB's implementation of SEM, through the class
 // \doxygen{otb}{SEMClassifier}. This class performs a stochastic version
 // of the EM algorithm, but instead of inheriting from
-// \doxygen{itk}{ExpectationMaximizationMixtureModelEstimator}, we choosed to
+// \doxygen{itk}{ExpectationMaximizationMixtureModelEstimator}, we chose to
 // inherit from \subdoxygen{itk}{Statistics}{ListSample< TSample >},
 // in the same way as \doxygen{otb}{SVMClassifier}.
 //
@@ -159,7 +159,7 @@ int main(int argc, char * argv[])
 //  Software Guide : BeginLatex
 //
 //  By default, \doxygen{otb}{SEMClassifier} performs initialization of
-// \code{ModelComponentBase} by as many instanciation of
+// \code{ModelComponentBase} by as many instantiation of
 // \subdoxygen{otb}{Statistics}{GaussianModelComponent} as the number of
 // classes to estimate in the mixture. Nevertheless, the user may add specific
 // distribution into the mixture estimation. It is permitted by the use of
@@ -182,7 +182,7 @@ int main(int argc, char * argv[])
 
 //  Software Guide : BeginLatex
 //
-//  Once the pipeline is instanciated. The segmentation by itself may be
+//  Once the pipeline is instantiated. The segmentation by itself may be
 // launched by using the \code{Update} function.
 //  Software Guide : EndLatex
 

Examples/Learning/SOMClassifierExample.cxx

@@ -74,7 +74,7 @@ int main(int argc, char* argv[])
 // As for the SOM learning step, we must define the types for the
 // \code{otb::SOMMap}, and therefore, also for the distance to be
 // used. We will also define the type for the SOM reader, which is
-// actually an \subdoxygen{otb}{ImageFileReader} which the appropiate
+// actually an \subdoxygen{otb}{ImageFileReader} which the appropriate
 // image type.
 //
 //  Software Guide : EndLatex

Examples/Learning/SOMExample.cxx

@@ -173,7 +173,7 @@ int main(int itkNotUsed(argc), char* argv[])
 //
 //  Software Guide : BeginLatex
 //
-// As an alternative to standart \code{SOMType}, one can decide to use
+// As an alternative to standard \code{SOMType}, one can decide to use
 // an \doxygen{otb}{PeriodicSOM}, which behaves like \doxygen{otb}{SOM} but
 // is to be considered to as a torus instead of a simple map. Hence, the
 // neighborhood behavior of the winning neuron does not depend on its location
@@ -268,7 +268,7 @@ int main(int itkNotUsed(argc), char* argv[])
 //
 //  Software Guide : BeginLatex
 //
-//  Now comes the intialization of the functors.
+//  Now comes the initialization of the functors.
 //
 //  Software Guide : EndLatex
 

Examples/Learning/SVMGenericKernelImageClassificationExample.cxx

@@ -98,7 +98,7 @@ int main(int argc, char* argv[])
 // section \ref{ssec:LearningFromImages} for an example of model
 // estimation and storage to a file).
 //
-// When using a user defined kernel, an explicit instanciation has
+// When using a user defined kernel, an explicit instantiation has
 // to be performed.
 //
 // Software Guide : EndLatex

Examples/Learning/SVMGenericKernelImageModelEstimatorExample.cxx

@@ -78,7 +78,7 @@ int main(int argc, char* argv[])