Publication :
Thermography data fusion and non-negative matrix factorization for the evaluation of cultural heritage objects and buildings

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Date
2018-08-24
Direction de publication
Direction de recherche
Titre de la revue
ISSN de la revue
Titre du volume
Éditeur
Springer
Projets de recherche
Structures organisationnelles
Numéro de revue
Résumé
The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively.
Description
Revue
Journal of Thermal Analysis and Calorimetry, 1-19 (2018)
DOI
https://doi.org/10.1007/s10973-018-7644-6
URL vers la version publiée
Mots-clés
Thermal image segmentation , Negative matrix factorization analysis , Gradient-descent-based multiplicative rules , Non-negative least squares (NNLS) active-set algorithm , Wavelet data fusion , Clustering
Citation
Type de document
article de recherche