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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We consider the problem of estimating convex boundaries from blurred andnoisy observations. In our model, the convolution of an intensity function $f$is observed with additive Gaussian white noise. The function $f$ is assumed tohave convex support $G$ whose boundary is to be recovered. Rather than directlyestimating the intensity function, we develop a procedure which is based onestimating the support function of the set $G$. This approach is closelyrelated to the method of geometric hyperplane probing, a well-known techniquein computer vision applications. We establish bounds that reveal how theestimation accuracy depends on the ill-posedness of the convolution operatorand the behavior of the intensity function near the boundary.

Recovering convex boundaries from blurred and noisy observations