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Fix incorrect autolinks in doc

This commit is contained in:
Simon Giraudot 2016-11-03 15:10:05 +01:00
parent ed583ac6e9
commit 3d667a7d26
1 changed files with 5 additions and 5 deletions
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10
Classification/doc/Classification/Classification.txt
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@ -10,7 +10,7 @@ This component implements a generalization of the algorithm described in \cgalCi
\section Classification_Organization Package Organization
Classification of point sets is achieved as follows:
%Classification of point sets is achieved as follows:
- some analysis is performed on the input point set;
- attributes are computed based on this analysis;
@ -29,11 +29,11 @@ Organization of the package.
\subsection Classification_analysis Analysis
Classification is based on the computation of local attributes of points. These attributes often require precomputed analysis structures: such data structures might be shared by several attributes and are therefore computed separately.
%Classification is based on the computation of local attributes of points. These attributes often require precomputed analysis structures: such data structures might be shared by several attributes and are therefore computed separately.
\cgal provides the following structures:
- `CGAL::Classification::Neighborhood` stores spatial searching structures and provides adapted queries on indexed points;
- `CGAL::Classification::Point_set_neighborhood` stores spatial searching structures and provides adapted queries for points;
- `CGAL::Classification::Local_eigen_analysis` precomputes covariance matrices on local neighborhood of points and stores the associated eigenvectors and eigenvalues;
- `CGAL::Classification::Planimetric_grid` is a 2D grid used for digital terrain modeling.
@ -99,7 +99,7 @@ The following code snippet shows how to add types to the classification object a
\subsection Classification_regularization Regularization
Classification is performed by minizing an energy over the input point set. This energy can be regularized with different methods. \cgal provides 3 different methods for classification, ranging from high speed / low quality to low speed / high quality:
%Classification is performed by minizing an energy over the input point set. This energy can be regularized with different methods. \cgal provides 3 different methods for classification, ranging from high speed / low quality to low speed / high quality:
- `CGAL::Point_set_classification::run()`
- `CGAL::Point_set_classification::run_with_local_smoothing()`
@ -213,7 +213,7 @@ The classification algorithm is designed to be as flexible as possible: the user
\section Classification_training Training
Classification is based on relationships between attributes and types. Each attribute has a specific weight and each pair of attribute/type has a specific effect. This means that the number of parameters to set up becomes rapidly large: if 6 attributes are used to classify between 4 classification types, 30 parameters have to be set up.
%Classification is based on relationships between attributes and types. Each attribute has a specific weight and each pair of attribute/type has a specific effect. This means that the number of parameters to set up becomes rapidly large: if 6 attributes are used to classify between 4 classification types, 30 parameters have to be set up.
Though it is possible to set them one by one, \cgal also provides a training algorithm that requires a small set of ground truth points provided by the user. More specifically, the user must provide, for each classification type he/she wants to classify, a set of known inliers among the input point set (for example, selecting one roof, one tree, one section of the ground). The training algorithm works as follows: