English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The preservation of our cultural heritage is of paramount importance. Thanksto recent developments in digital acquisition techniques, powerful imageanalysis algorithms are developed which can be useful non-invasive tools toassist in the restoration and preservation of art. In this paper we propose asemi-supervised crack detection method that can be used for high-dimensionalacquisitions of paintings coming from different modalities. Our datasetconsists of a recently acquired collection of images of the Ghent Altarpiece(1432), one of Northern Europe's most important art masterpieces. Our goal isto build a classifier that is able to discern crack pixels from the backgroundconsisting of non-crack pixels, making optimal use of the information that isprovided by each modality. To accomplish this we employ a recently developednon-parametric Bayesian classifier, that uses tensor factorizations tocharacterize any conditional probability. A prior is placed on the parametersof the factorization such that every possible interaction between predictors isallowed while still identifying a sparse subset among these predictors. Theproposed Bayesian classifier, which we will refer to as conditional Bayesiantensor factorization or CBTF, is assessed by visually comparing classificationresults with the Random Forest (RF) algorithm.

Bayesian crack detection in ultra high resolution multimodal images of paintings