Commit 237ace1c authored by Emmanuel Christophe's avatar Emmanuel Christophe
Browse files

DOC: Adding doxygen documentation and correcting mistakes

parent 4415a8b6
......@@ -27,7 +27,7 @@
namespace otb
{
/** \class otbBSplineDecompositionImageFilter
/** \class BSplineDecompositionImageFilter
* \brief This class is an evolution of the itk::BSplineDecompositionImageFilter to handle
* huge images with this interpolator. For more documentation, please refer to the original
* class.
......
......@@ -24,7 +24,7 @@
namespace otb
{
/** \class otbLabelizeConnectedThresholdImageFilter
/** \class LabelizeConnectedThresholdImageFilter
* \brief
*
*/
......
......@@ -26,8 +26,8 @@
namespace otb
{
/** \class otbLabelizeImageFilterBase
* \brief
/** \class LabelizeImageFilterBase
* \brief Base class for filter labelizing image region based on segmentation.
*
*/
template <class TInputImage, class TOutputImage, class TFilter>
......
......@@ -25,6 +25,10 @@
namespace otb {
/** \class CumulantsForEdgeworth
* \brief Helper class for KullbackLeiblerDistanceImageFilter. Please refer to KullbackLeiblerDistanceImageFilter.
*
*/
template < class TInput >
class CumulantsForEdgeworth
{
......@@ -59,7 +63,10 @@ class CumulantsForEdgeworth
namespace Functor {
/** \class KullbackLeiblerDistance
* \brief Functor for KullbackLeiblerDistanceImageFilter. Please refer to KullbackLeiblerDistanceImageFilter.
*
*/
template < class TInput1, class TInput2, class TOutput >
class KullbackLeiblerDistance
{
......
......@@ -29,7 +29,7 @@
namespace otb {
/** \class KullbackLeiblerSupervizedDistance
/** \class KullbackLeiblerSupervizedDistanceImageFilter
* \brief Implements KullbackLeibler distance over Edgeworth approximation,
* between a Neighborhood and a predefined Region of Interest.
*
......
......@@ -35,8 +35,13 @@ namespace otb
*
*
*/
namespace Functor {
namespace Functor
{
/** \class AssociativeSymmetricalSum
* \brief Functor used with the AssociativeSymmetricalSumImageFilter.
*/
template< class TInput1, class TInput2, class TOutput>
class ITK_EXPORT AssociativeSymmetricalSum
{
......
......@@ -27,6 +27,7 @@
namespace otb
{
/** \class FillGapsFilter
* \brief To be documented
*/
class ITK_EXPORT FillGapsFilter : public itk::ProcessObject
{
......
......@@ -25,7 +25,11 @@
namespace otb
{
/**
*\class LineCorrelationDetectorImageFilter
* \brief To be documented
*
*/
template <class TInputImage,
class TOutputImage,
......
......@@ -35,8 +35,13 @@
namespace otb
{
namespace Functor
{
/** \class BayesianFunctor
* \brief Functor for the bayesian fusion filter. Please refer to BayesianFusionFilter.
*
*/
template <class TInputMultiSpectral,
class TInputMultiSpectralInterp,
class TInputPanchro,
......@@ -125,8 +130,9 @@ namespace otb
*/
/***** END TODO ***/
/** \class StreamingStatisticsVectorImageFilter
* \brief Baesian fusion filter. Contribution of Julien Radoux
/** \class BayesianFusionFilter
* \brief Bayesian fusion filter. Contribution of Julien Radoux
*
* Please refer to D. Fasbender, J. Radoux and P. Bogaert,
* Bayesian Data Fusion for Adaptable Image Pansharpening,
......@@ -148,9 +154,6 @@ namespace otb
*
*/
template <class TInputMultiSpectralImage,
class TInputMultiSpectralInterpImage,
class TInputPanchroImage,
......
......@@ -24,7 +24,7 @@ PURPOSE. See the above copyright notices for more information.
namespace otb
{
/** \class Filename
/** \class FileName
* \brief This class represents a file name
*
* It is derived from the ossimFilename class, which allows to manipulate a
......
......@@ -91,12 +91,12 @@ public:
std::string keyname;
KeyType type;
KeyTypeDef() {}
KeyTypeDef(std::string _keyname, KeyType _type)
{
keyname = _keyname;
type = _type;
}
KeyTypeDef() {}
KeyTypeDef(std::string _keyname, KeyType _type)
{
keyname = _keyname;
type = _type;
}
} ;
......
......@@ -22,24 +22,25 @@
#include "svm.h"
//FIXME: shouldn't it be in the Functor namespace?
namespace otb
{
/** \class NonGaussianRBFKernelFunctor
* \brief Performs an RBF kernel evaluation that better suit sample distribution with high Kurtosis.
*
* It is of kind
* $\exp\left( - \gamma \sum_i | x_i^\alpha - y_i^\alpha |^\beta \right)$
* where $0 \leqslant \alpha \leqslant 1$ and
* $0 \leqslant \beta \leqslant 2$.
*
* Variables to be instanciated (through \code SetValue \endcode) are:
* Alpha (def=1), Beta (def=2) and Gamma (def 1.0).
* */
class NonGaussianRBFKernelFunctor
: public GenericKernelFunctorBase
{
public:
/** Non Gaussian RBF.
* Performs an RBF kernel evaluation that better suit sample distribution
* with high Kurtosis.
* It is of kind
* $\exp\left( - \gamma \sum_i | x_i^\alpha - y_i^\alpha |^\beta \right)$
* where $0 \leqslant \alpha \leqslant 1$ and
* $0 \leqslant \beta \leqslant 2$.
*
* Variables to be instanciated (through \code SetValue \endcode) are:
* Alpha (def=1), Beta (def=2) and Gamma (def 1.0).
* */
double operator() ( const svm_node * x, const svm_node * y,
const svm_parameter & param ) const;
......
......@@ -9,36 +9,37 @@
#include "otbGaussianModelComponent.h"
/** \class SEMClassifier
* \brief This class implements the Stochastic Expectation
* Maximization algorithm to perform an estimation of a mixture model.
*
* The first template argument is the type of the target sample
* data. This estimator expects one or more model component objects
* of the classes derived from the ModelComponentBase. The actual
* component (or module) parameters are updated by each component.
* Users can think this class as a strategy or a integration point
* for the SEM procedure.
*
* The number of classes (SetNumberOfClasses), the initial
* proportion (SetInitialProportions), the input sample (SetSample),
* the model components (AddComponent), and the maximum iteration
* (SetMaximumIteration) are required. The SEM procedure terminates
* when the current iteration reaches the maximum iteration or the model
* parameters converge.
*
* The difference from ExpectationMaximizationMixtureModelEstimator is
* that SEMClassifier include the maximum a posteriori decition on each
* sample. The class is to be seen as a classification and not an estimator.
*
* <b>Recent API changes:</b>
* N/A
*
* \sa ModelComponentBase, GaussianModelComponent
*/
namespace otb {
namespace otb {
/** \class SEMClassifier
* \brief This class implements the Stochastic Expectation
* Maximization algorithm to perform an estimation of a mixture model.
*
* The first template argument is the type of the target sample
* data. This estimator expects one or more model component objects
* of the classes derived from the ModelComponentBase. The actual
* component (or module) parameters are updated by each component.
* Users can think this class as a strategy or a integration point
* for the SEM procedure.
*
* The number of classes (SetNumberOfClasses), the initial
* proportion (SetInitialProportions), the input sample (SetSample),
* the model components (AddComponent), and the maximum iteration
* (SetMaximumIteration) are required. The SEM procedure terminates
* when the current iteration reaches the maximum iteration or the model
* parameters converge.
*
* The difference from ExpectationMaximizationMixtureModelEstimator is
* that SEMClassifier include the maximum a posteriori decition on each
* sample. The class is to be seen as a classification and not an estimator.
*
* <b>Recent API changes:</b>
* N/A
*
* \sa ModelComponentBase, GaussianModelComponent
*/
template< class TInputImage, class TOutputImage >
class ITK_EXPORT SEMClassifier
: public itk::Statistics::SampleClassifier<
......
......@@ -36,7 +36,7 @@ PURPOSE. See the above copyright notices for more information.
namespace otb
{
/** \class otbMapProjection
/** \class MapProjection
* \brief This is the base class for all geographic projections (UTM, Lambert, ...)
*
* All derived class assume that the latitude and longitude are given according to the
......
......@@ -37,7 +37,7 @@ PURPOSE. See the above copyright notices for more information.
namespace otb
{
/** \class otbTileMapTransform
/** \class TileMapTransform
* \brief to do
**/
......
......@@ -27,41 +27,10 @@
namespace otb
{
/** \class otbPolarimetricSynthesisFilter
/** \class MultiChannelsPolarimetricSynthesisFilter
* \brief
*
* This class compute the polarimetric synthesis from a radar vector image,
* depening on the polarimetric architecture :
* \begin{enumerate}
* \item HH_HV : two channels are available: $S_{HH}$ and $S_{HV}$.
* Emit polarisation is fixed to horizontal orientation: $\psi_{i}=0$ and $\chi_{i}=0$.
* \item VV_VH : two channels are available: $S_{VV}$ and $S_{VH}$.
* Emit polarisation is fixed to vertical orientation: $\psi_{i}=90$ and $\chi_{i}=0$.
* \item HH_HV_VV : three channels are available: $S_{HH}$, $S_{HV}$ and $S_{VV}$.
* we make the assumption that cross polarisation are reciprocal ($S_{HV} = S_{VH}$).
* \item HH_HV_VH_VV: four channels are available $S_{HH}$, $S_{HV}$, $S_{VH}$ and $S_{VV}$.
* \end{enumerate}
*
* \begin{enumerate}
* \item emissionH : if two images are contained into the vector, emissionH enables to determine that
* the type of architecture is HH_HV.
* \item emissionV : In the same way, with onlyt two images emissionV enables to determine that the type
* of architecture is VH_VV.
* \end{enumerate}
*
* To resolve the synthesis, four parameters are required: $\psi_{i}$ , $\chi_{i}$, $\psi_{r}$ and $\chi_{r}$.
* These parameters depend on the polarimetric architecture describe below.
*
* The result of the synthesis is a scalar image. Three modes are available:
* \begin{enumerate}
* \item none: set the four parameters;
* \item co: $\psi_{r} = \psi_{i}$ and $\chi_{r} = \chi_{i}$
* \item cross: $\psi_{r} = \psi_{i} + 90$ and $\chi_{r} = -\chi_{i}$
* \end{enumerate}
*
* This class is parameterized over the type of the input images and
* the type of the output image. It is also parameterized by the
* operation to be applied, using a Functor style.
*
*/
......
......@@ -27,7 +27,7 @@
namespace otb
{
/** \class otbPolarimetricSynthesisFilter
/** \class PolarimetricSynthesisFilter
* \brief
*
* This class compute the polarimetric synthesis from two to four radar images,
......@@ -36,7 +36,7 @@ namespace otb
* \item HH_HV : two channels are available: $S_{HH}$ and $S_{HV}$.
* Emit polarisation is fixed to horizontal orientation: $\psi_{i}=0$ and $\chi_{i}=0$.
* \item VV_VH : two channels are available: $S_{VV}$ and $S_{VH}$.
* Emit polarisation is fixed to vertical orientation: $\psi_{i}=90$ and $\chi_{i}=0$.
* Emit polarisation is fixed to vertical orientation: $\psi_{i}=90^\circ$ and $\chi_{i}=0$.
* \item HH_HV_VV : three channels are available: $S_{HH}$, $S_{HV}$ and $S_{VV}$.
* we make the assumption that cross polarisation are reciprocal ($S_{HV} = S_{VH}$).
* \item HH_HV_VH_VV: four channels are available $S_{HH}$, $S_{HV}$, $S_{VH}$ and $S_{VV}$.
......@@ -49,7 +49,7 @@ namespace otb
* \begin{enumerate}
* \item none: set the four parameters;
* \item co: $\psi_{r} = \psi_{i}$ and $\chi_{r} = \chi_{i}$
* \item cross: $\psi_{r} = \psi_{i} + 90$ and $\chi_{r} = -\chi_{i}$
* \item cross: $\psi_{r} = \psi_{i} + 90^\circ$ and $\chi_{r} = -\chi_{i}$
* \end{enumerate}
*
* This class is parameterized over the type of the input images and
......
......@@ -23,7 +23,7 @@
namespace otb
{
/** \class FullResolutionImageWidget
* \brief
* \brief Widget for the full resolution window in viewer.
*
*/
template <class TPixel>
......
......@@ -25,7 +25,7 @@
namespace otb
{
/** \class ImageWidgetBoxForm
* \brief
* \brief Widget to draw boxes on the viewer
*
*/
class ImageWidgetBoxForm
......
......@@ -31,7 +31,7 @@
namespace otb
{
/** \class ImageWidgetPolygonForm
* \brief
* \brief Widget to draw polygons on the viewer
*
*/
template<class TValue = double>
......
......@@ -23,7 +23,7 @@
namespace otb
{
/** \class ZoomableImageWidget
* \brief
* \brief Widget for the zoom window in viewer.
*
*/
template <class TPixel>
......
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