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 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.
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Journal articlePermalink
https://hdl.handle.net/10161/15603Published Version (Please cite this version)
10.1109/ICDSP.2013.6622710Publication 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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Show full item recordScholars@Duke
Ingrid Daubechies
James B. Duke Distinguished Professor of Mathematics and Electrical and Computer Engineering
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
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