A two-stream neural network architecture for the detection and analysis of cracks in panel paintings

Museums all over the world store a large variety of digitized paintings and other works of art with significant historical value. Over time, these works of art deteriorate, making them lose their original splendour. For paintings, cracks and paint losses are the most prominent types of deterioration, mainly caused by environmental factors, such as fluctuations in temperature or humidity, improper storage conditions and even physical impacts. We propose a neural network architecture for the detection of crack patterns in paintings, using visual acquisitions from different modalities. The proposed architecture is composed of two neural network streams, one is a fully connected neural network while the other consists of a multiscale convolutional neural network. The convolutional neural network plays a leading role in the crack classification task, while the fully connected neural network plays an auxiliary role. To reduce the overall computational complexity of the proposed method, we use morphological filtering as a pre-processing step to safely exclude areas of the image that do not contain cracks and do not need further processing. We validate the proposed method on a multimodal visual dataset from the Ghent Altarpiece, a world famous polyptych by the Van Eyck brothers. The results show an encouraging performance of the proposed approach compared to traditional machine learning methods and the state-of-the-art Bayesian Conditional Tensor Factorization (BCTF) method for crack detection.

ISBN:9781510634794

Publication year:2021

Accessibility:Open