approach that explicitly addresses pattern analysis over surfaces. The meshLBP extends the LBP [@ojala] to triangle meshes. The main idea behind the meshLBP is that triangles play the role of pixels and the 8-neighbor connectivity in an image is ideally substituted by a 6-neighbor connectivity around triangles. Rings on the mesh are computed using a uniform, region growing, triangle-based expansion. From the practical point of view, the meshLBP encodes a pattern efficiently, providing a compact representation of it.

The edgeLBP {#sec:method}
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We extend the LBP to surfaces using rings defined on the basis of a sphere-mesh intersection. In Section \[ss:LBP\_imagesc\] we briefly sum up the definition of the LBP definition. Our extension to surface tessellations is described in Section \[ss:edgeLBP\_def\], while Section \[ss:descriptor\] details the edgeLBP descriptor and the distance adopted to compare two descriptors.

Local Binary Pattern for gray-scale images {#ss:LBP_imagesc}
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The *Local binary pattern (LBP)* and its variants prove to be a good solution for the classification of patterns in images [@LIU2017]. Given a gray-scale image $I$, the LBP describes the pattern in $I$ coding the local variation of the gray-scale values (encoded with a function $h:I\rightarrow [0,255]$) around each pixel of $I$. More extensively, for 