Analysis of craquelure patterns in historical painting 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.