Automatic Stones Classification through a CNN-Based Approach.

This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of "Sistema per l'Identificazione di Lapidei Per Immagini"), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution.

Spectroscopic, microchemical and petrographic analyses of plasters from ancient buildings in Lamezia Terme (Calabria, Southern Italy).

This work shows the results of the spectroscopic, microchemical and petrographic study carried out on six plasters coming from three important residential buildings of the 18th century, located in Lamezia Terme (Catanzaro, Southern Italy). To study the provenance of the raw materials used to make the plasters, one sample of limestone and two samples of sand were also collected from the quarries near Lamezia Terme and compared with the historical plasters. Samples were studied by polarized optical microscopy (OM), X-ray powder diffraction (XRPD), scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) and Raman spectroscopy. The results of these analyses allowed to determine the mineralogical, petrographical and chemical characteristics of the plasters, identify the pigments used for their coloration and provide useful information about the building techniques, the raw materials employed and the production technology of plasters during the 18th century in Lamezia Terme. SEM-EDS microanalysis also revealed the presence of gold and silver on the surface of two samples.