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

Ridge detection is a classical tool to extract curvilinear features in imageprocessing. As such, it has great promise in applications to material scienceproblems; specifically, for trend filtering relatively stable atom-shapedobjects in image sequences, such as Transmission Electron Microscopy (TEM)videos. Standard analysis of TEM videos is limited to frame-by-frame objectrecognition. We instead harness temporal correlation across frames throughsimultaneous analysis of long image sequences, specified as a spatio-temporalimage tensor. We define new ridge detection algorithms to non-parametricallyestimate explicit trajectories of atomic-level object locations as a continuousfunction of time. Our approach is specially tailored to handle temporalanalysis of objects that seemingly stochastically disappear and subsequentlyreappear throughout a sequence. We demonstrate that the proposed method ishighly effective and efficient in simulation scenarios, and delivers notableperformance improvements in TEM experiments compared to other material sciencebenchmarks.

Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation