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Butler, L., 2023. Southeast Asian illuminated manuscripts: a study of the maritime silk road using scientific imaging & AI. PhD, Nottingham Trent University.
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Abstract
In this project, the British Library’s maritime Southeast Asian (SEA) illuminated manuscript collection was analysed via data visualisation and machine learning techniques to complement the holistic multimodal large-scale identification of artistic materials using visible to near-infrared (VNIR) reflectance spectral imaging (SI) and other complementary analysis techniques. Other techniques used included X-Ray fluorescence, external reflection-Fourier transform infrared, ultraviolet-visible-short wave infrared and Raman spectroscopies, all of which have shown to perform well for material identification when used together.
To condense large-scale spectral imaging data into manageable datasets, a new automated clustering and pigment identification informed grouping method was developed to reduce billions of VNIR spectra collected during large-scale SI surveys into smaller sets of unique spectral groups. Using this methodology meant that most artistic materials throughout the collection could be characterised and have their distributions mapped in less than 200 groups, where each group possessed a unique pigment mixture used within 18th-19th century maritime SEA. By using pigment mixture maps produced after grouping, 43 different unique pigment mixtures could be mapped and detected within 50 different manuscripts. One of the main findings showed an increasing European influence in the region as years progressed, but a tendency to mainly use traditional materials as late as the early-mid-19th century, e.g. With a new detection of bone ash white being common in early Javanese manuscripts. The results offered by this study provided great insight into the use of artistic materials during this period and can be used now as a fundamental base for future research into maritime SEA and by extension the maritime silk road.
Additionally, a novel holistic multimodal clustering technique was also developed which allowed for the automated clustering of SI data created via different complementary techniques. And in addition to this, a new classification method was proposed which would allow for future large-scale SI data analysis and material identification to be performed as part of a hybrid clustering
and classification approach.
| Item Type: | Thesis | ||||||||||||
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| Creators: | Butler, L. | ||||||||||||
| Contributors: |
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| Date: | March 2023 | ||||||||||||
| Rights: | The copyright in this work is held by 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 author. | ||||||||||||
| Divisions: | Schools > School of Science and Technology | ||||||||||||
| Record created by: | Laura Ward | ||||||||||||
| Date Added: | 15 Nov 2024 16:07 | ||||||||||||
| Last Modified: | 15 Nov 2024 16:07 | ||||||||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/52582 |
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