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

We show that the recently introduced L1TV functional can be used toexplicitly compute the flat norm for co-dimension one boundaries. While thisobservation alone is very useful, other important implications for imageanalysis and shape statistics include a method for denoising sets which are notboundaries or which have higher co-dimension and the fact that using the flatnorm to compute distances not only gives a distance, but also an informativedecomposition of the distance. This decomposition is made to depend on scaleusing the "flat norm with scale" which we define in direct analogy to the L1TVfunctional. We illustrate the results and implications with examples andfigures.

L1TV Computes the Flat Norm for Boundaries