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

Matching patches from a noisy image to atoms in a dictionary of patches is akey ingredient to many techniques in image processing and computer vision. Byrepresenting with a single atom all patches that are identical up to aradiometric transformation, dictionary size can be kept small, therebyretaining good computational efficiency. Identification of the atom in bestmatch with a given noisy patch then requires a contrast-invariant criterion. Inthe light of detection theory, we propose a new criterion that ensures contrastinvariance and robustness to noise. We discuss its theoretical grounding andassess its performance under Gaussian, gamma and Poisson noises.

Template matching with noisy patches: A contrast-invariant GLR test