Machine learning analysis of illuminated Southeast Asian manuscripts using complementary noninvasive imaging techniques (Conference Presentation)

Luke Butler, S. Kogou, Yu Li, C. Cheung, Haida Liang, A. T. Gallop, P. Garside, Christina Duffy
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Abstract

The complementary use of X-ray fluorescence (XRF) mapping, spectral imaging, and Raman mapping, allows for the analysis and identification of important artistic materials used in the production and illustration of illuminated manuscripts. This project uses combined non-invasive imaging techniques to analyse 17th – 19th century manuscripts from the British Library’s Southeast Asia Collections so that more can be understood about the adoption and evolution of artistic materials and techniques used in Maritime Southeast Asia. Using multiple different imaging techniques has shown to provide positive results, however, a consequence of this is the collection of large amounts of data, necessitating the automatic and unsupervised analytical techniques used in machine learning. Data collected in-situ at the British Library using macro-XRF mapping, macro-Raman mapping, and Spectral Imaging, will be analysed using a range of machine learning techniques to cluster pixel information representing materials used in southeast Asian manuscripts.
使用互补无创成像技术的东南亚照明手稿的机器学习分析(会议报告)
x射线荧光(XRF)制图、光谱成像和拉曼制图的互补使用,允许分析和识别用于照明手稿制作和插图的重要艺术材料。该项目采用非侵入性成像技术,分析大英图书馆东南亚馆藏的17 - 19世纪手稿,以便更多地了解东南亚海事艺术材料和技术的采用和演变。使用多种不同的成像技术已经显示出积极的结果,然而,这样做的后果是收集大量数据,需要在机器学习中使用自动和无监督的分析技术。使用宏观xrf制图、宏观拉曼制图和光谱成像在大英图书馆现场收集的数据将使用一系列机器学习技术进行分析,以聚类代表东南亚手稿中使用的材料的像素信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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