To preserve the heritage of our past, we need audacious solutions that belong to the world of the future!
In the GRIEG project, Noemi Zabari uses deep learning techniques, the subcategory of machine learning, to analyze the crack patterns in historical paintings, especially panel paintings. At the SPIE Optical Metrology conference, Dr. Zabari discussed the results of the analysis conducted on the set of selected craquelure patterns using self-learning processes.
If you are interested, we encourage you to read the conference paper: https://doi.org/10.1117/12.2593982