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Proceedings Paper

A Bayesian decision model for watercolour analysis
Author(s): Vassiliki Kokla; Alexandra Psarrou; Vassilis Konstantinou
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Paper Abstract

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.

Paper Details

Date Published: 16 July 2007
PDF: 9 pages
Proc. SPIE 6618, O3A: Optics for Arts, Architecture, and Archaeology, 66180S (16 July 2007); doi: 10.1117/12.725666
Show Author Affiliations
Vassiliki Kokla, Univ. of Westminster (United Kingdom)
Alexandra Psarrou, Univ. of Westminster (United Kingdom)
Vassilis Konstantinou, Univ. of Westminster (United Kingdom)


Published in SPIE Proceedings Vol. 6618:
O3A: Optics for Arts, Architecture, and Archaeology
Costas Fotakis; Luca Pezzati; Renzo Salimbeni, Editor(s)

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