Bayesian Classification methods can be applied to images of watercolour paintings in order to characterize blue
and green pigments used in these paintings. Pigments found in watercolour paintings are semi-transparent
materials and their analysis provides important information on the date, the painter, the place of the production
of watercolour paintings and generally on the authenticity of these works of art. However, watercolour pigments
are difficult to characterize because their intensity depends on the amount of liquid spread during painting and
the reflective properties of the underlying support. The method describedin this paper is non-destructive, non
invasive, does not involve sampling and can be applied in situ. The methodology is based on the photometric
properties of pigments and produce computational models which classify diverse types of pigments found in
watercolour paintings. These pigments are photographed in the visible and infrared area of electromagnetic
spectrum and models based on statistical characteristics of intensity values using a mixture of Gaussian functions
are created. Finally the pigments are classified using a Bayesian classification algorithm to process the generate models.
Inks constitute the main element in Medieval manuscripts and their examination and analysis provides an invaluable source of information on the authenticity of the manuscripts, the number of authors involved and dating of the manuscripts. Most existing methods for the analysis of ink materials are based on destructive testing techniques that require the physicochemical sampling of data. Such methods cannot be widely used because of the historical and cultural value of manuscripts. In this paper we present a novel approach for discriminating and identifying inks based on the correlations of image variations under visible and infrared illumination. Such variations are studied using co-occurrence matrices and detect the behavior of the inks during the scripting process.
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