Emeline Pouyet
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Emeline Pouyet has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Giovanni Delnevo
University of Bologna, Cesena, Italy
1 shared publication - Laurence de Viguerie
LAMS (Laboratoire d’Archéologie Moléculaire et Structurale), CNRS UMR 8220, Sorbonne Université, 4 Place Jussieu, Paris 75005, France
1 shared publication - Lingxi Liu
Department of Art History, Musicology and Theatre Studies, Ghent University, Ghent, Belgium
1 shared publication - Philippe Walter
Laboratoire d’Archéologie Moléculaire et Structurale, CNRS-Sorbonne Université, 75005 Paris, France
1 shared publication - Silvia Mirri
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
1 shared publication - Tsveta Miteva
Sorbonne Université, CNRS, Laboratoire de Chimie Physique – Matiére et Rayonnement, LCPMR, F-75005 Paris, France
1 shared publication
Latest Publications
- Open Access
Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review
Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.
Open AccessNeural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review
Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.