This paper proposes a semi-automated methodology based on a sequence of analysis processes performed multispectral images of artworks and aimed at the extraction of vector maps regarding their state of . The graphic relief of the artwork represents the main instrument of communication and of information and data acquired on cultural heritage during restoration. Despite the widespread of informatics tools, currently, these operations are still extremely subjective and require high execution and costs. In some cases, manual execution is particularly complicated and almost impossible to carry . The methodology proposed here allows supervised, partial automation of these procedures avoids and drastically reduces the work times, as it makes a vector drawing by extracting the areas from the raster images. We propose a procedure for color segmentation based on /independent component analysis (PCA/ICA) and SOM neural networks and, as a case study, the results obtained on a set of multispectral reproductions of a painting on canvas.
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Amura et al. (2020) studied this question.