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

Microscopic images of vortex fields are important for understanding phase transitions in superconductors. These optical images include noise with high and variable intensity, hence are manually processed to extract numerical data from underlying meshes. The current thinning and skeletonization algorithms struggle to find connected meshes in these noisy images and often output edge pixels with numerous gaps and superfluous branching point. We have developed a new symmetric thinning algorithms to extract from such highly noisy images 1-pixel wide skeletons with theoretical guarantees. The resulting skeleton is converted into a polygonal mesh that has only polygonal edges at sub-pixel resolution. The experiments on over 100 real and 6250 synthetic images establish the state-of-the-art in extracting optimal meshes from highly noisy images.

Polygonal Meshes of Highly Noisy Images based on a New Symmetric Thinning Algorithm with Theoretical Guarantees