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Cluster Analysis of IR Thermography Data for Differentiating Glass Types in Historical Leaded-Glass Windows
Infrared thermography is a fast, non-destructive and contactless testing technique which is increasingly used in heritage science. The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of...
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Published in: | Applied sciences 2020-06, Vol.10 (12), p.4255 |
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description | Infrared thermography is a fast, non-destructive and contactless testing technique which is increasingly used in heritage science. The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. Subtle visual color differences between panes, implying a variation in coloring metal ions, was not always distinguished by IRT. Nevertheless, data clustering assisted infrared thermography shows potential as an efficient and swift method for documenting the material intervention history of leaded-glass windows during or in preparation of conservation treatments. |
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The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. Subtle visual color differences between panes, implying a variation in coloring metal ions, was not always distinguished by IRT. Nevertheless, data clustering assisted infrared thermography shows potential as an efficient and swift method for documenting the material intervention history of leaded-glass windows during or in preparation of conservation treatments.</description><identifier>ISSN: 2076-3417</identifier><identifier>EISSN: 2076-3417</identifier><identifier>DOI: 10.3390/app10124255</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Analysis ; Cameras ; Chemical properties ; Cluster analysis ; Clustering ; Conservation ; Cooling ; Destructive testing ; Epoxy resins ; Fourier transforms ; Glass ; hierarchical clustering ; History ; Infrared analysis ; Infrared imaging ; Infrared thermometers ; Inspection ; IR thermography ; k-means clustering ; leaded-glass windows ; Materials ; Metal ions ; Nondestructive testing ; Paints ; Principal components analysis ; Statistics ; Thermography ; Windows</subject><ispartof>Applied sciences, 2020-06, Vol.10 (12), p.4255</ispartof><rights>COPYRIGHT 2020 MDPI AG</rights><rights>2020. 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The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. Subtle visual color differences between panes, implying a variation in coloring metal ions, was not always distinguished by IRT. Nevertheless, data clustering assisted infrared thermography shows potential as an efficient and swift method for documenting the material intervention history of leaded-glass windows during or in preparation of conservation treatments.</description><subject>Analysis</subject><subject>Cameras</subject><subject>Chemical properties</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Conservation</subject><subject>Cooling</subject><subject>Destructive testing</subject><subject>Epoxy resins</subject><subject>Fourier transforms</subject><subject>Glass</subject><subject>hierarchical clustering</subject><subject>History</subject><subject>Infrared analysis</subject><subject>Infrared imaging</subject><subject>Infrared thermometers</subject><subject>Inspection</subject><subject>IR thermography</subject><subject>k-means clustering</subject><subject>leaded-glass windows</subject><subject>Materials</subject><subject>Metal ions</subject><subject>Nondestructive testing</subject><subject>Paints</subject><subject>Principal components analysis</subject><subject>Statistics</subject><subject>Thermography</subject><subject>Windows</subject><issn>2076-3417</issn><issn>2076-3417</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1r3DAQNaWFhjSn_gFBjsWpvixbx2XTJgsLhbKlRzGWRxttvJYraSn776uNQ8nMYYaZ9x7Dm6r6zOidEJp-hXlmlHHJm-ZddcVpq2ohWfv-Tf-xuknpQEtoJjpGr6rn9XhKGSNZTTCek08kOLL5SXZPGI9hH2F-OpN7yEBciOTeO4cRp-wh-2lPHkZIiezOMybiJ_LoUw7RWxjJFmHAoV4Av_00hL_pU_XBwZjw5rVeV7--f9utH-vtj4fNerWtraQi172VjVK2VbpjMDSoWnSWOsC-aUUjO6tdx_Cy5pZp6DllYAVopgQ0tGfiutosukOAg5mjP0I8mwDevAxC3BuI2dsRDZVMMK0lk8rJjrO-B9EMLfRWA3cUi9btojXH8OeEKZtDOMXiVTK8GKq0kowW1N2C2kMR9ZMLOYItOeDR2zCh82W-UqKTF-d5IXxZCDaGlCK6_2cyai7fNG--Kf4BJ-iQ2w</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Hillen, Michaël</creator><creator>Legrand, Stijn</creator><creator>Dirkx, Yarince</creator><creator>Janssens, Koen</creator><creator>Van der Snickt, Geert</creator><creator>Caen, Joost</creator><creator>Steenackers, Gunther</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1991-2443</orcidid><orcidid>https://orcid.org/0000-0001-9985-0678</orcidid><orcidid>https://orcid.org/0000-0001-5859-8402</orcidid><orcidid>https://orcid.org/0000-0002-6546-7395</orcidid></search><sort><creationdate>20200601</creationdate><title>Cluster Analysis of IR Thermography Data for Differentiating Glass Types in Historical Leaded-Glass Windows</title><author>Hillen, Michaël ; 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The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. 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subjects | Analysis Cameras Chemical properties Cluster analysis Clustering Conservation Cooling Destructive testing Epoxy resins Fourier transforms Glass hierarchical clustering History Infrared analysis Infrared imaging Infrared thermometers Inspection IR thermography k-means clustering leaded-glass windows Materials Metal ions Nondestructive testing Paints Principal components analysis Statistics Thermography Windows |
title | Cluster Analysis of IR Thermography Data for Differentiating Glass Types in Historical Leaded-Glass Windows |
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