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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Since thermal imagery offers a unique modality to investigate pain, the U.S.National Institutes of Health (NIH) has collected a large and diverse set ofcancer patient facial thermograms for AI-based pain research. However,differing angles from camera capture between thermal and visible sensors hasled to misalignment between Visible-Thermal (VT) images. We modernize theclassic computer vision task of image registration by applying and modifying agenerative alignment algorithm to register VT cancer faces, without the needfor a reference or alignment parameters. By registering VT faces, wedemonstrate that the quality of thermal images produced in the generative AIdownstream task of Visible-to-Thermal (V2T) image translation significantlyimproves up to 52.5\%, than without registration. Images in this paper havebeen approved by the NIH NCI for public dissemination.

A Generative Approach for Image Registration of Visible-Thermal (VT) Cancer Faces