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

We present an approach for controlling relaxation parameters in variable conductance diffusion. This approach incorporates a Bayesian classifier to perform a partial labeling of an image, followed by a diffusion step. Conductance values between pixels are controlled by statistical measurements made of the partial classification. Several iterations follow, interleaving partial labeling with diffusion steps until a convergence or stopping criterion is met. The method is suitable for performing diffusion within multi-valued images. It consistently controls relaxation parameters, even in the presence of noise. The method is presented along with results on phantom and MR images.

Using statistical pattern recognition techniques to control variable conductance diffusion