Publicomos

Notice (permalien)

Réf.

43240

Type

conference item

Titre

Analysis of craquelure patterns in historical painting using image processing along with neural network algorithms

Langues

English

Auteurs

Zabari, Noemi

Date

13/07/2021

Titre de conférence

SPIE Optical Metrology, 2021

Lieu de conférence

online

Mots-clés

paintings / craquelures / information and communication technologies / conservation of cultural heritage / monitoring / analysis of materials / quantitative analysis

Résumé en anglais

Recent advances in technology have brought major breakthroughs in deep learning techniques. In this work, the author will elaborate on such techniques for output data of image processing performed on craquelure patterns in historical paintings. Historical painted objects, especially panel paintings, with their long environmental history, exhibit complex crack patterns called craquelures. These are cracks in paintings that can be referred to as ‘edge fractures’ since they are formed from the free surface. The analysis has been conducted on the set of selected craquelure patterns to which a recent deep learning method, i.e. Neural Networks algorithm is implemented and the results of such a self-learning process are discussed.

Document joint

published.pdf

Licence

Creative Commons Attribution-Non Commercial-No Derivatives (BY-NC-ND)