Mr. Luke Butler Profile

Luke Butler

at Nottingham Trent Univ

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Publications (6)

Proceedings Article | 20 June 2021 Presentation

Multimodal remote sensing for the monitoring and identification of moisture and salts on heritage buildings

Sotiria Kogou, Yu Li, Sammy Cheung, Haida Liang, David Thickett, Florence Liggins, Luke Butler

Proceedings Volume 11784, 117840V (2021) https://doi.org/10.1117/12.2593919

KEYWORDS: Short wave infrared radiation, Buildings, Remote sensing, Spatial resolution, Raman spectroscopy, Laser sources, Imaging spectroscopy, Remote Raman spectroscopy, Continuous wave operation, Spectral resolution

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Proceedings Article | 20 June 2021 Presentation

Machine learning analysis of complementary multimodal spectral imaging data from a large manuscript collection

Luke Butler, Sotiria Kogou, Alexander Hogg, Yu Li, Alessandra Vichi, Annabel Gallop, Haida Liang

Proceedings Volume 11784, 1178405 (2021) https://doi.org/10.1117/12.2593918

KEYWORDS: Imaging spectroscopy, Machine learning, Associative arrays, Visualization, Reflectivity, FT-IR spectroscopy, Spectroscopy, Luminescence, Hyperspectral imaging, Data analysis

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This study sets out to analyse the artistic materials used in the maritime Southeast Asian manuscript collection at the British Library. To gain a full understanding of how artistic practises may have developed over time and changed between regions, it is necessary to perform large scale scientific analysis. Visible/NIR spectral imaging is an efficient method of collecting spectral reflectance data which can be used to distinguish different materials. Recent advancements in automatic data collection have meant that the volume of data collected has greatly increased, making traditional approaches to data analysis impossible to perform in a timely manner. Machine learning provides a viable solution to this as it can be used to automatically cluster millions of spectra into smaller, more manageable numbers of distinct spectral groups. Self-organising Maps are used as the building blocks of an algorithm which can perform clustering of large collections of spectral imaging data. Spectral reflectance alone is often not enough to perform pigment identification, consequently other complementary techniques are required. Advances in spectral imaging mean that each of these complementary techniques has a corresponding imaging modality. The machine learning approach developed in this project can be adapted to allow for the clustering of multimodal spectral imaging data including VIS/NIR hyperspectral imaging, macro-X-Ray fluorescence mapping, macro-Raman mapping, and Fourier transform infrared mapping. For multimodal clustering, each modality can be clustered individually and then brought together to produce a single cluster map which is a more refined representation of the material distribution than that produced from any individual spectral imaging modality.   A visualisation tool has also been developed for the easy interpretation and interrogation of spectral imaging data cubes and cluster maps for entire collections. Both the visualisation tool and clustering method will be made accessible to the cultural heritage community through an online DIGILAB platform.

Proceedings Article | 20 June 2021 Presentation

Investigations into the composition and deterioration processes of 16th century Limoges enamels using Optical Coherence Tomography (OCT), spectral imaging and x-ray fluorescence (XRF)

Margaret Read, Andrew Meek, Florence Liggins, Chi Shing Cheung, Sotiria Kogou, Luke Butler, Capucine Korenberg, Denise Ling, Haida Liang

Proceedings Volume 11784, 1178413 (2021) https://doi.org/10.1117/12.2593898

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Limoges enamels along with a lot of glass-based objects suffer from deterioration that affects the condition, structure and longevity of the pieces affected. Understanding the mechanisms and causality of this deterioration can aid in conserving and preserving the wares in an optimal manner. Limoges enamels are composed of multiple layers of glass over a metal substrate, multiple colours and levels of opacity are used with gildings and paillons to create vibrancy and decoration within the artwork. A main concern is the exposure of the glassware to moisture and pollutants that can cause crizzling, cracking and spalling of the enamel and cause detrimental and irreversible damage. Detection and analysis of the moisture content, progression of the gel layer (a compositionally modified layer formed on the surface of the glass due to the attack from moisture or pollutants), and overall subsurface condition of the enamels can help give insight into the level of deterioration. Furthermore, some enamels and certain colours show worse levels of deterioration. Is this a product of the environmental conditions of storage or the composition of the enamels? Non-invasive analytical techniques are a priority in analysis of cultural objects; therefore, multiple non-invasive techniques were employed to survey and analyse eight Limoges enamels from the sixteenth century in the British Museum collection. These techniques were 810nm Ultra-High-Resolution Optical Coherence Tomography (UHR OCT), 400-2300nm Fibre Optical Reflectance Spectroscopy (FORS), X-Ray Fluorescence (XRF), VIS/NIR (400nm-850nm) spectral imaging (PRISMS) and Shortwave Infrared (1000 – 2500nm) spectral imaging (SWIR). UHR OCT images the structure and level of deterioration throughout the enamel by showing the extent of the gel layer in the glass, as well as the structure and condition throughout the top layer. SWIR can be used to give information on the level of hydration in the glass across the entire enamel, this is mapped in 2D over the entire enamel for analysis. XRF has been used widely in glass analysis and can help identify the composition of the glass. PRISMS results give information on the spectra in the visible/NIR region complementing elemental analysis by XRF, it is useful in this investigation as a standardised way to analyse spectra and categorise the spectra and colour of an enamel. By using a combination of all of these techniques, investigations were conducted to understand if there is a link between the composition and deterioration of the selected sixteenth century Limoges enamels. Results using the OCT and SWIR analytical techniques have already shown a strong link between the level of hydration and the level of structural deterioration in these Limoges enamels, further analysis showed there was a correlation between the depth of the gel layer and the level of hydration. Furthermore, when incorporating all the techniques, initial findings have shown interesting insights into how the composition can affect the deterioration of these enamels.

Proceedings Article | 13 December 2020 Presentation

Extracting maps of meaning from archival data: testing the applicability of AI-based spectral clustering on multiband images of historic cartographic collections

Lucia Pereira-Pardo, Florence Liggins, Luke Butler, Sotiria Kogou, Haida Liang

Proceedings Volume 11784, 1178406 (2020) https://doi.org/10.1117/12.2593910

KEYWORDS: Imaging systems, Raman spectroscopy, Imaging spectroscopy, Data acquisition, X-ray fluorescence spectroscopy, Visualization, Visual analytics, Spectroscopy, Scanners, Reflectance spectroscopy

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Proceedings Article | 22 July 2019 Presentation

Long-range remote spectroscopy for wall paintings and architectural interiors (Conference Presentation)

Yu Li, Chi Shing Cheung, Sotiria Kogou, Alex Hogg, Florence Liggins, Luke Butler, Haida Liang

Proceedings Volume 11058, 110580U (2019) https://doi.org/10.1117/12.2527614

KEYWORDS: Spectroscopy, Raman spectroscopy, Laser induced fluorescence, Imaging spectroscopy, Laser induced breakdown spectroscopy, Reflectivity, Visible radiation, Near infrared, Molecular lasers, Molecular spectroscopy

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Proceedings Article | 22 July 2019 Presentation

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

Luke Butler, Sotiria Kogou, Yu Li, Chi Shing Cheung, Haida Liang, Annabel Gallop, Paul Garside, Christina Duffy

Proceedings Volume 11058, 110581M (2019) https://doi.org/10.1117/12.2527576

KEYWORDS: Machine learning, Imaging spectroscopy, Associative arrays, X-ray fluorescence spectroscopy, Raman spectroscopy

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