of samples ($P$) on them, together with different $R$ associated to the $N_r$-th ring (called $R_{max}$). The value of $R$ is based on the size of the patterns in it: we randomly picked 3 models of that dataset and choose one or more $R_{max}$ values that were properly scaled for the dataset. The parameters $N_r$ and $P$ are initially set with what we consider the default settings: $P=15$, $N_r=5$. Similarly we consider $h=L-channel$ of the CIELAB color space as the default setting of the function $h$. Different choices of $h$, $P$ and $N_r$ are discussed for the Artifacts dataset.

#### CPP dataset {#cpp-dataset .unnumbered}

We tested the edgeLBP on this dataset using the default settings and adapting the $R_{max}$ to the size of the wanted pattern ($R_{max}=0.04mm$), in what in this paper is called *Run1*. As baseline methods to compare against the edgeLBP descriptor we consider two variations of the color histograms. *Hist1* outputs descriptors based on a 16-bin histogram normalized on his minimal and maximal $L$ values. *Hist2* is similar, but no normalization is applied to the values of $L$. In addition, we also consider the meshLBP descriptor as implemented in the Matlab toolbox [@meshLBP]. Figure \[fig:ccp\_res\](Top) reports the 