Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications

Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational effort...

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Bibliographic Details
Published in:Journal of imaging 2022-10, Vol.8 (10), p.284
Main Authors: Scatigno, Claudia, Festa, Giulia
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
convolutional neural networks
Cultural heritage
Cultural resources
Data analysis
Data compression
Data imaging
Data processing
Data reduction
Datasets
Deep Learning
Feature extraction
imaging
Investigations
Machine learning
Magnetic resonance imaging
Materials science
Microbiology
Neural networks
Neutrons
Optimization
Review
segmentation
Tomography
X ray imagery
X-rays
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Summary:Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational effort, to find benchmarks and extract features, to improve the resolution, and reproducibility performances of the imaging data. Currently, no Neutron Imaging combined with learning algorithms was applied on cultural heritage domain, but future applications could help to solve challenges of this research field. Here, a review of pioneering works to exploit the use of Machine Learning and Deep Learning models applied to X-ray imaging and Neutron Imaging data processing is reported, spanning from biomedicine, microbiology, and materials science to give new perspectives on future cultural heritage applications.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging8100284