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Title: Evaluating multispectral imaging processing methodologies for analysing cultural heritage documents
Author: Giacometti, A.
ISNI:       0000 0004 5363 1586
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2014
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Multispectral imaging is a method for acquiring image data over a series of wavelengths across the light spectrum. In combination with several image processing methods, it has become a valuable tool for the enhancement and recovery of information from cultural heritage documents. It enables the identification of distinct physical characteristics of the document and its material components by measuring their interaction with light. In this dissertation I describe an approach to evaluate multispectral imaging processing methods in the context of cultural heritage documents. I begin by framing a digital model, or phantom, of multispectral images of a document composed of interacting layers of text and other materials. Further, I apply a series of image processing methods to the phantom in order to extract and separate those interacting layers and I qualitatively assess the results of each of those methods. I proceed then to propose and apply a quantitative method, based on mutual information, to evaluate the efficacy of those image processing methods. Next, I describe the administration of a series of treatments that degrade the writing or otherwise degrade samples from a real document, and the acquisition of multispectral images from those samples. Finally, I report on the recovery of information via image processing of the acquired experimental data. The results of the image processing of the experimental data are then quantitatively evaluated using the proposed method, and qualitatively described. As multispectral imaging in combination with image processing methods become increasingly used in cultural heritage documents, we need a deeper understanding of how these methods perform in the analysis of this particular data. I contribute to this understanding in two important ways: I introduce a formal methodology to study the methods involved in processing and analysing the multispectral data, and provide a framework to continue testing and developing new methods.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available