definition, see Section \[ss:LBP\_imagesc\].

Given the surface tessellation $T$, its *edgeLBP descriptor* is labeled $D_T$. The entry $D_T(n,m)$ is defined as the histogram that counts how many vertices have an edgeLBP value equal to $m$ on the $ring_n$. Since in the experiments we are mostly interested in the distribution of the edgeLBP values, we adopt $\frac{D_T}{n_v}$ as the edgeLBP descriptor, where $n_v$ is the number of the admissible vertices. Through this normalization of $T$ we achieve robustness to the number of vertices of the surface representation.

We define the dissimilarity between two tessellations $A$ and $B$ as the distance between their corresponding edgeLBP descriptors $D_A$ and $D_B$. Since the edgeLBP can be thought as a matrix, any feature vector distance is suitable to evaluate the similarity between two edgeLBP descriptors. We analysed the Euclidean distance between matrices, the *Earth Mover’s Distance* as defined in [@Rubner:2000] and the *Bhattacharyya distance*. The Bhattacharyya distance $d_{Bha}$ between two distributions $\phi$ and $\psi$ of a scalar random variable $X$ has the following definition: $$d_{Bha}(\phi,\psi)=\sqrt{1-BC(\phi,\psi)}, \qquad BC(\phi,\psi)=\sum\limits_{x\in X} \sqrt{\phi(x)\psi(x)},$$ where $BC$ is called the *Bhattacharyya coefficient*. Then, for a set of surface tessellations, the dissimilarity values are stored in a *distance matrix* $DM(i,j)=d(D_i,D_j)$, where $d$ 