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Bayesian crack detection in ultra high resolution multimodal images of paintings

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Date
2013-12-06
Authors
Cornelis, B
Yang, Y
Vogelstein, JT
Dooms, A
Daubechies, I
Dunson, D
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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 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.
Type
Journal article
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https://hdl.handle.net/10161/15603
Published Version (Please cite this version)
10.1109/ICDSP.2013.6622710
Publication Info
Cornelis, B; Yang, Y; Vogelstein, JT; Dooms, A; Daubechies, I; & Dunson, D (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 https://hdl.handle.net/10161/15603.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Daubechies

Ingrid Daubechies

James B. Duke Distinguished Professor of Mathematics and Electrical and Computer Engineering
Dunson

David B. Dunson

Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more.  We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers.  We are also interested in new inference framework and in studying theoretical properties
Alphabetical list of authors with Scholars@Duke profiles.
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