Using the Unsupervised Mixture of Gaussian Models for Multispectral Non-destructive Evaluation of the Replica of Botticelli’s “The Birth of Venus”; Journal of Nondestructive Evaluation; Vol. 42, iss. 2

Using the Unsupervised Mixture of Gaussian Models for Multispectral Non-destructive Evaluation of the Replica of Botticelli’s “The Birth of Venus”; Journal of Nondestructive Evaluation; Vol. 42, iss. 2

Bibliographic Details
Parent link:Journal of Nondestructive Evaluation.— .— New York: Springer Science+Business Media LLC.
Vol. 42, iss. 2.— 2023.— Article number 38, 9 p.
Other Authors: Qi Li, Hai Zhang, Jue Hu, Stefano S. Sfarra, Mostacci M. Miranda, Dazhi Yang, Georges M. Marc, Vavilov V. P. Vladimir Platonovich, Maldague X. Xavier
Summary:Title screen
With increasing attention paid to the protection of cultural relics, non-destructive testing (NDT) technologies are thought to be profoundly rewarding, resulting in a widespread uptake of feature-extraction algorithms and defect-detection techniques. Among various alternatives, infrared thermography (IRT) and Terahertz time-domain spectroscopy (THz-TDS) are non-invasive in nature and thus are appropriate for applications involving ancient buildings and artworks. The present study is motivated by the fact that online/offline background segmentation algorithms based on the Gaussian mixture model and widely used in video processing to distinguish between foreground and background, can be successfully integrated as a feature extraction tool with NDT. Since IRT and THz-TDS image sequences resemble a video, the image length and width can be taken as the first two dimensions, and time can serve as the third one. Such sequences can be processed effectively by the background segmentation algorithms, thus can help detect defects of different types at varying depths. The experimental section of the paper considers a tempera painting (a replica of Botticelli’s “The Birth of Venus”) with artificiallyintroduced defects. For benchmarking purposes, the background segmentation algorithms (based on a mixture of Gaussian models) are compared with the fast Fourier transform and principal component analysis to demonstrate the superior performance of the proposed novel algorithms
Текстовый файл
AM_Agreement
Language:English
Published: 2023
Subjects:
Non-destructive testing
Cultural heritage
Painting
Infrared thermography
Image processing
Terahertz
Machine learning
электронный ресурс
труды учёных ТПУ
Online Access:https://doi.org/10.1007/s10921-023-00947-9
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=682004

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