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Simplify description in the Triangulated Surface Mesh Segmentation user manual 

Co-authored-by: Sebastien Loriot <sloriot.ml@gmail.com>
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Nuno Nobre 2023-07-05 14:55:02 +01:00 committed by GitHub
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Surface_mesh_segmentation/doc/Surface_mesh_segmentation/Surface_Mesh_Segmentation.txt
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@ -128,7 +128,7 @@ where:
Note both terms of the energy function, \f$ e_1 \f$ and \f$ e_2 \f$, are always non-negative. Note both terms of the energy function, \f$ e_1 \f$ and \f$ e_2 \f$, are always non-negative.
The first term of the energy function provides the contribution of the soft clustering probabilities. The first term of the energy function provides the contribution of the soft clustering probabilities.
The second term of the energy function is a geometric criterion that is larger the closer to \f$\pm\pi\f$, i.e. the flatter, the dihedral angle between two adjacent facets not in the same cluster is. The second term of the energy function is a geometric criterion that is larger the closer to \f$\pm\pi\f$ the dihedral angle between two adjacent facets not in the same cluster is.
The smoothness parameter makes this geometric criterion more or less prevalent. The smoothness parameter makes this geometric criterion more or less prevalent.
Assigning a high value to the smoothness parameter results in a small number of segments (since constructing a segment boundary would be expensive). Assigning a high value to the smoothness parameter results in a small number of segments (since constructing a segment boundary would be expensive).