Bayesian crack detection in ultra high resolution multimodal images of paintings
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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. © 2013 IEEE.
Published Version (Please cite this version)10.1109/ICDSP.2013.6622710
Publication InfoCornelis, Bruno Ingmar; Daubechies, Ingrid; Dooms, A; Dunson, David B; Vogelstein, Joshua T; & Yang, Y (2013). Bayesian crack detection in ultra high resolution multimodal images of paintings. 2013 18th International Conference on Digital Signal Processing, DSP 2013. 10.1109/ICDSP.2013.6622710. Retrieved from http://hdl.handle.net/10161/15603.
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Visiting Assistant Professor of Mathematics
James B. Duke Professor of Mathematics and Electrical and Computer Engineering
Arts and Sciences Professor of Statistical Science
Development of novel approaches for representing and analyzing complex data. A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty. In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecol
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