Hyperspectral crack detection in paintings

Several approaches to the crack detection of paintings are available for grayscale and color images, and recently also for spectral images. However, the approaches that are used for the multivariate data are either using a marginal approach or requiring a data reduction which enable the use of grayscale operators. In this study, the crack detection task is addressed with a spectral processing expressed in a fullband and vector approach. By using distance functions in the ordering relations and crack detection method, the metrological constraints required by such important cultural heritage objects are respected. The performances of the crack detection methods are assessed with artificial images which combine real spectral images of known properties and simple probabilistic crack model, and also with images from cracked paintings.