diff --git a/Surface_mesh_segmentation/doc/Surface_mesh_segmentation/Surface_Mesh_Segmentation.txt b/Surface_mesh_segmentation/doc/Surface_mesh_segmentation/Surface_Mesh_Segmentation.txt
index c947aac7dd7..b618ef20ce0 100644
--- a/Surface_mesh_segmentation/doc/Surface_mesh_segmentation/Surface_Mesh_Segmentation.txt
+++ b/Surface_mesh_segmentation/doc/Surface_mesh_segmentation/Surface_Mesh_Segmentation.txt
@@ -102,12 +102,12 @@ The energy function minimized using alpha-expansion graph cut algorithm \cgalCit
          <td>
                 \f$ E(\bar{x}) = \sum\limits_{f \in F} e_1(f, x_f) + \lambda \sum\limits_{ \{f,g\} \in N} e_2(x_f, x_g) \f$
 
-                \f$ e_1(f, x_f) = -log(max(P(f|x_f), \epsilon)) \f$
+                \f$ e_1(f, x_f) = -\log(\max(P(f|x_f), \epsilon_1)) \f$
 
                 \f$ e_2(x_f, x_g) =
                 \left \{
                 \begin{array}{rl}
-                        -log(\theta(f,g)/\pi) &\mbox{ $x_f \ne x_g$} \\
+                        -\log(w\max(1 - |\theta(f,g)|/\pi, \epsilon_2)) &\mbox{ $x_f \ne x_g$} \\
                         0 &\mbox{ $x_f = x_g$}
                 \end{array}
                 \right \} \f$
@@ -119,8 +119,8 @@ where:
   - \f$x_f\f$ denotes the cluster assigned to facet \f$f\f$,
   - \f$P(f|x_p)\f$ denotes the probability of assigning facet \f$f\f$ to cluster \f$x_p\f$,
   - \f$\theta(f,g)\f$ denotes the dihedral angle between neighboring facets \f$f\f$ and \f$g\f$:
-         concave angles and convex angles are weighted by 1 and 0.1 respectively,
-  - \f$\epsilon\f$ denotes the minimal probability threshold,
+         convex angles, \f$[-\pi, 0]\f$, and concave angles, \f$]0, \pi]\f$, are weighted by \f$w=0.08\f$ and \f$w=1\f$, respectively,
+  - \f$\epsilon_1, \epsilon_2\f$ denote minimal probability and angle thresholds, respectively,
   - \f$\lambda \in [0,1]\f$ denotes a smoothness parameter.
          </td>
   </tr>
@@ -128,7 +128,7 @@ where:
 
 Note both terms of the energy function, \f$ e_1 \f$ and \f$ e_2 \f$, are always non-negative.
 The first term of the energy function provides the contribution of the soft clustering probabilities.
-The second term of the energy function is a geometric criterion that is larger when two adjacent facets sharing a sharp and concave edge are not in the same cluster.
+The second term of the energy function is a geometric criterion that is larger the closer to \f$\pm\pi\f$, i.e. the flatter, the dihedral angle between two adjacent facets not in the same cluster is.
 The 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).