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/*!
* \defgroup graphical_models Graphical Models
* \defgroup inference Inference Algorithms
* \defgroup spaces Space Types
* \defgroup operators Operators
* \defgroup functions Function Types
*/
// DOXYGEN EXAMPLES
/*!
* \example quick_start.cxx
* \example grid_potts.cxx
* \example interpixel_boundary_segmentation.cxx
* \example one_to_one_matching.cxx
* \example io_graphical_model.cxx
* \example gibbs.cxx
* \example inference_types.cxx
* \example markov-chain.cxx
* \example space_types.cxx
* \example swendsenwang.cxx
* \example opengmBuild.cxx
*/
// DOXYGEN MAINPAGE
/*! \mainpage OpenGM
*
* \section Introduction
* OpenGM is a C++ template library for defining discrete graphical models and
* performing inference on these models, using a wide range of state-of-the-art
* algorithms.
* \n
* No restrictions are imposed on the factor graph to allow for higher-order
* factors and arbitrary neighborhood structures.
* \n
* Large models with repetitive structure are handled efficiently because
* (i) functions that occur repeatedly need to be stored only once, and (ii)
* distinct functions can be implemented differently, using different encodings
* alongside each other in the same model.
* \n
* Several parametric functions (e.g.~metrics), sparse and dense value tables are
* provided and so is an interface for custom C++ code.
* \n
* Algorithms are separated by design from the representation of graphical models
* and are easily exchangeable.
* \n
* OpenGM, its algorithms, HDF5 file format and command line tools are modular and
* extendible.
*
* \section Handbook
* A very detailed <B>OpenGM-handbook for users and developers </B> is available at the
* <A HREF="http://hci.iwr.uni-heidelberg.de/opengm2"> OpenGM webside </A HREF>
* or use the
* <A HREF="http://hci.iwr.uni-heidelberg.de/opengm2/download/opengm-2.0.2-beta-manual.pdf"> direct link </A HREF>
* to download the handbook in pdf form.
* \n
* The handbook covers everything from installation instructions to usage examples but also gives insides in the
* mathematical foundations of graphical models.
*
*
* \section Modules
*
* - \ref graphical_models
* - \ref inference
* - \ref spaces
* - \ref operators
* - \ref functions
* .
* .
* \section examples Examples
*
* \subsection cppexamples C++ Examples
* - \link quick_start.cxx Quick Start \endlink
* - \link grid_potts.cxx N-class segmentation on a 2d grid with a Potts model \endlink
* - \link interpixel_boundary_segmentation.cxx Segmentation in the dual / boundary domaine \endlink
* - \link one_to_one_matching.cxx One-to-one Matching \endlink
* - \link markov-chain.cxx Simple markov chain \endlink
* - \link inference_types.cxx Usage of different inference algorithms \endlink
* - \link space_types.cxx Usage of different space types \endlink
* - \link io_graphical_model.cxx save / load a graphical model from / to hdf5 \endlink
* .
*
* \subsection matlabexamples MatLab Examples
* - \link testGridCreation.m Create a grid-structured model \endlink
* - \link testAddUnaries.m Adding several unaries at ones (this is faster) \endlink
*
* - \link opengmBuild.cxx Example for build models from Matlab with own mex-file \endlink
* - \link opengmBuildGrid.cxx Build grid models from Matlab with own mex-file \endlink
* .
*
* \subsection pythonexamples Python Examples
* - \link add_functions.py Adding functions to a model \endlink
* - \link add_factors_and_functions.py Adding factors and functions to a model \endlink
* - \link add_multiple_unaries.py Adding several unaries at ones (this is faster) \endlink
* - \link markov_chain.py Simple markov chain \endlink
* - \link inference_bp.py Inference with LBP \endlink
* - \link inference_graphcut.py Inference with GraphCut \endlink
* .
*
*/
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