Bayesian crack detection in high resolution data

Bruno Cornelis, Ann Dooms, Ingrid Daubechies, David Dunson

Onderzoeksoutput: Meeting abstract (Book)

Samenvatting

We propose a semi-supervised crack detection method that can be used for high-dimensional and multimodal acquisitions of paintings. Our dataset consists of a recent collection of images of the Ghent Altarpiece (1432), one of Northern Europe's most important art masterpieces. We 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.
Originele taal-2English
TiteliTWIST 14 international Traveling Workshop on Interactions between Sparse models and Technology, Namur, Belgium
StatusPublished - 7 aug 2014
EvenementiTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology - Namur, Belgium
Duur: 27 aug 201429 aug 2014

Conference

ConferenceiTWIST'14 international Traveling Workshop on Interactions between Sparse models and Technology
Land/RegioBelgium
StadNamur
Periode27/08/1429/08/14

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