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 factorisations to characterise any conditional probability. A prior is placed on the parameters of the factorisation 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 factorisation or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm.
Cornelis, B, Yang, Y, Vogelstein, J, Dooms, A, Daubechies, I & Dunson, D 2013, Bayesian crack detection in ultra high resolution multimodal images of paintings. in 18th International Conference on Digital Signal Processing (DSP 2013) Proceedings. International Conference on Digital Signal Processing Proceedings, IEEE, 18th International Conference on Digital Signal Processing (DSP 2013), Santorini, Greece, 1/07/13.
Cornelis, B., Yang, Y., Vogelstein, J., Dooms, A., Daubechies, I., & Dunson, D. (2013). Bayesian crack detection in ultra high resolution multimodal images of paintings. In 18th International Conference on Digital Signal Processing (DSP 2013) Proceedings (International Conference on Digital Signal Processing Proceedings). IEEE.
@inproceedings{c45ae1464bf847aca76aaca95daf13d1,
title = "Bayesian crack detection in ultra high resolution multimodal images of paintings",
abstract = "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 factorisations to characterise any conditional probability. A prior is placed on the parameters of the factorisation 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 factorisation or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm.",
keywords = "Ghent Altarpiece, classification, crack detection, nonparametric Bayes, variable selection, tensor factorisation",
author = "Bruno Cornelis and Yun Yang and Joshua Vogelstein and Ann Dooms and Ingrid Daubechies and David Dunson",
year = "2013",
language = "English",
isbn = "978-1-4673-5805-7",
series = "International Conference on Digital Signal Processing Proceedings",
publisher = "IEEE",
booktitle = "18th International Conference on Digital Signal Processing (DSP 2013) Proceedings",
note = "18th International Conference on Digital Signal Processing (DSP 2013) ; Conference date: 01-07-2013 Through 03-07-2013",
url = "http://dsp2013.dspconferences.org/",
}