Investigation of the complementary use of non-invasive techniques for the holistic analysis of paintings and automatic analysis of large scale spectral imaging data

Tools

Kogou, S, 2017. Investigation of the complementary use of non-invasive techniques for the holistic analysis of paintings and automatic analysis of large scale spectral imaging data. PhD, Nottingham Trent University.

Preview

Text
Sotiria Kogou 2018.pdf - Published version
Download (12MB) | Preview

Abstract

The analysis of painting materials and methods is acknowledged for providing important information to art history. This study illustrates a detailed examination of the characteristics, advantages and capabilities that the combined application of a variety of non-invasive techniques, ranging from spectral imaging and optical coherence tomography (OCT), to fibre optics reflectance spectroscopy (FORS), X-ray Fluorescence (XRF) and Raman spectroscopy, has to offer. The analysis of painting materials is seen under the prism of a holistic examination of different types of cultural heritage objects. More specifically, the limitations that the individual techniques face and, most importantly, how their complementary use can overcome them are thoroughly investigated through the examination of a large and heterogeneous statistical sample, in a completely novel way. The heterogeneity of the sample is related both to the painting materials (i.e pigments, binding media and substrates) and the degradation level (i.e. paintings stored in storages of museum and murals of caves that are exposed in the environmental conditions of the desert).
For the extraction of accurate conclusions about the painting materials and methods applied in a specific period, the examination of large number of artworks of this period is required. PRISMS, the spectral imaging system developed by our group enables the time efficient imaging of large painting surfaces, leading to the acquisition of large scale spectral imaging data, which makes such an analysis faster, more cost-effective and less laborious without diminishing the quality of the results. This study proposes methods based on the statistical analysis for the automatic processing of the spectral imaging data in two directions: the revealing of information that is obvious under visual observation and clustering of the spectral information.
With regards to the automatic revealing of hidden information, the potential of principal component analysis (PCA) and independent principal analysis (ICA), two of the most commonly used statistical analysis methods, were examined giving very good results.
In addition, the development of a new method for the automatic clustering of large scale spectral information based on the 'Self-organised mapping' (SOM) method is presented. The spectral feature of the analysed areas in the UV-VIS/NIR (400-900 nm) is indicative for its pigment composition, therefore the automatic clustering of the pixel-level spectral information that the PRISMS system provides can classify the areas according to their pigment composition. The application of statistical analysis methods in the preliminary stage of the analysis of large number of artworks (e.g. large painting collections) of large surface painting areas (e.g. murals) is of significant importance; as they highlight the areas that should be examined in detail.
The multimodal non-invasive approach was applied on the examination of three artworks of significant importance for East Asian art history; the cave 465 of the Mogao complex in China, the export Chinese watercolor paintings from the collections of the Victoria and Albert (V&A) museum and the Royal Horticulture Society (RHS) and the Selden map. The examination of these three works of art, in addition to providing a wide and heterogeneous sample for the detailed examination of the multi-modal approach, has also helped addressing several historical questions.

Item Type:	Thesis
Creators:	Kogou, S.
Date:	September 2017
Rights:	This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed to the owner(s) of the Intellectual Property Rights.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	19 Feb 2018 11:32
Last Modified:	19 Feb 2018 12:08
URI:	https://irep.ntu.ac.uk/id/eprint/32752