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/

Zendy Plus

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

Zendy Tools

Zendy Open

One of the most important but yet partly neglected steps in quantitative microstructural analysis is microscopic image segmentation. Despite major advances in the currently available segmentation methods and first of all, machine learning segmentation, the metal industry in many cases still relies on segmentation by thresholding. Based on microstructural images of low-alloy steels, this work will show to what extent machine learning segmentation can accomplish a significant quality improvement compared to the traditional thresholding method.

Application of Trainable Segmentation to Microstructural Images Using Low-alloy Steels as an Example