Recently a new trend towards a more systematic use of Reflectance Hyperspectral imaging (HSI) has emerged in major museums. Extensive acquisition of HSI data opens up new research topics in terms of comparative analysis, creation and population of spectral databases, linking and crossing information. However, a full exploitation of these big-size data-sets unavoidably raises new issues about data-handling and processing methods. Along with statistical and multivariate analysis, new solutions can be borrowed from the Artificial Intelligence (AI) area, using Machine Learning (ML) and Deep Learning (DL) methods. In this work different algorithms based on multivariate analysis and Artificial Intelligence methods are comparitevely applied to process HSI data acquired on three Picasso’ paintings from the Museu Picasso collection in Barcellona. By using a “data-mining approach” the HSI-data are examined to unveil new correlations and extract embedded information.
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