Analysis of craquelure patterns in historical paintings using image processing along with Neural Network algorithms

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.