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A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers
Computational Spectral Imaging Lab, School of Computing, University of Eastern Finland, Joensuu, Finland; Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh. (MPI, AASS)ORCID iD: 0000-0001-7387-6650
Institute of Photonics, University of Eastern Finland, Joensuu, Finland.
Computational Spectral Imaging Lab, School of Computing, University of Eastern Finland, Joensuu, Finland; Institute of Photonics, University of Eastern Finland, Joensuu, Finland.
2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 16, article id 4379Article in journal (Refereed) Published
Abstract [en]

In the field of cultural heritage, applied dyes on textiles are studied to explore their great artistic and historic values. Dye analysis is essential and important to plan correct restoration, preservation and display strategy in museums and art galleries. However, most of the existing diagnostic technologies are destructive to the historical objects. In contrast to that, spectral reflectance imaging is potential as a non-destructive and spatially resolved technique. There have been hardly any studies in classification of dyes in textile fibers using spectral imaging. In this study, we show that spectral imaging with machine learning technique is capable in preliminary screening of dyes into the natural or synthetic class. At first, sparse logistic regression algorithm is applied on reflectance data of dyed fibers to determine some discriminating bands. Then support vector machine algorithm (SVM) is applied for classification considering the reflectance of the selected spectral bands. The results show nine selected bands in short wave infrared region (SWIR, 1000–2500 nm) classify dyes with 97.4% accuracy (kappa 0.94). Interestingly, the results show that fairly accurate dye classification can be achieved using the bands at 1480nm, 1640 nm, and 2330 nm. This indicates possibilities to build an inexpensive handheld screening device for field studies.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 20, no 16, article id 4379
Keywords [en]
Spectral imaging, classification, logistic regression, cultural heritage, dyes, SVM
National Category
Computer graphics and computer vision Computer Sciences
Research subject
Computerized Image Analysis; Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-96709DOI: 10.3390/s20164379ISI: 000564589400001Scopus ID: 2-s2.0-85089212753OAI: oai:DiVA.org:oru-96709DiVA, id: diva2:1632417
Note

Funding agency:

Ministry of Posts, Telecommunication and Information Technology (ICT Division), Government of People's Republic of Bangladesh 56.00.0000.28.33.042.15-530

Available from: 2022-01-26 Created: 2022-01-26 Last updated: 2025-02-01Bibliographically approved

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Rahaman, G. M. Atiqur

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