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Abstract
Identification of animal species in medieval parchment manuscripts is highly relevant in cultural heritage studies. Usually, species identification is performed with slightly invasive methods. In this study, we propose a contactless methodology based on reflectance spectrophotometry (ultraviolet–visible–near-infrared) and a machine learning approach for data analysis. Spectra were recorded from both historical and modern parchments crafted from calf, goat, and sheep skins. First, a continuous wavelet transform was performed on the spectral data as a preprocessing step. Then, a semisupervised neural network with a 2-component architecture was applied to the preprocessed data. The network architecture chosen was CWT-CNN (continuous wavelet transform–convolutional neural network), which, in this case, is composed of a convolutional autoencoder and a single-layer dense network classifier. Species classification on holdout historical parchments was attained with a mean accuracy of 79%. The analysis of Shapley additive explanations values highlighted the main spectral ranges responsible for species discrimination. Our study shows that the animal species signature is encoded in a wide band-convoluted wavelength range rather than in specific narrow bands, implying a complex phenotype expression that influences the light scattering by the material. Indeed, the overall skin composition, in both micro- and macroscopic physicochemical properties, is relevant for animal identification in parchment manuscripts.
Original language | English |
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Article number | 0101 |
Pages (from-to) | 0101 |
Number of pages | 9 |
Journal | Intelligent Computing |
Volume | 3 |
DOIs | |
Publication status | Published - 17 Oct 2024 |
Keywords
- parchment
- machine learning
- wavelet transforms
- convolutional neural networks
- species identification
- UV-Vis-NIR spectroscopy
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Dive into the research topics of 'Animal Species Identification in Historical Parchments by Continuous Wavelet Transform– Convolutional Neural Network Classifier Applied to Ultraviolet–Visible–Near-Infrared Spectroscopic Data'. Together they form a unique fingerprint.Projects
- 1 Finished
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CÉCI – Consortium of high performance computing centers
CHAMPAGNE, B. (PI), Lazzaroni, R. (PI), Geuzaine , C. (CoI), Chatelain, P. (CoI) & Knaepen, B. (CoI)
1/01/18 → 31/12/22
Project: Research
Equipment
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High Performance Computing Technology Platform
Champagne, B. (Manager)
Technological Platform High Performance ComputingFacility/equipment: Technological Platform
Press/Media
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University of Namur Researchers Highlight Recent Research in Emerging Technologies (Animal Species Identification in Historical Parchments by Continuous Wavelet Transform-Convolutional Neural Network Classifier Applied to ...)
Deparis, O., Mayer, A., Bouhy, J. & Gravis, D.
31/10/24
1 item of Media coverage
Press/Media: Expert Comment