Bayesian crack detection in ultra high resolution multimodal images of paintings
This work addresses crack detection for art preservation, but it appears incremental as it applies an existing Bayesian method to a new dataset without demonstrating clear advancements.
The authors tackled the problem of detecting cracks in high-resolution multimodal images of paintings, specifically using a dataset of the Ghent Altarpiece, and proposed a semi-supervised Bayesian classifier (CBTF) that was visually compared to Random Forest, though no concrete performance numbers were provided.
The preservation of our cultural heritage is of paramount importance. Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of art. In this paper we propose a semi-supervised crack detection method that can be used for high-dimensional acquisitions of paintings coming from different modalities. Our dataset consists of a recently acquired collection of images of the Ghent Altarpiece (1432), one of Northern Europe's most important art masterpieces. Our goal is to build a classifier that is able to discern crack pixels from the background consisting of non-crack pixels, making optimal use of the information that is provided by each modality. To accomplish this we employ a recently developed non-parametric Bayesian classifier, that uses tensor factorizations to characterize any conditional probability. A prior is placed on the parameters of the factorization such that every possible interaction between predictors is allowed while still identifying a sparse subset among these predictors. The proposed Bayesian classifier, which we will refer to as conditional Bayesian tensor factorization or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm.