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

Image registration is a crucial task in signal processing, but it oftenencounters issues with stability and efficiency. Non-learning registrationapproaches rely on optimizing similarity metrics between fixed and movingimages, which can be expensive in terms of time and space complexity. Thisproblem can be exacerbated when the images are large or there are significantdeformations between them. Recently, deep learning, specifically convolutionalneural network (CNN)-based methods, have been explored as an effective solutionto the weaknesses of non-learning approaches. To further advance learningapproaches in image registration, we introduce an attention mechanism in thedeformable image registration problem. Our proposed approach is based on aTransformer framework called AiR, which can be efficiently trained on GPGPUdevices. We treat the image registration problem as a language translation taskand use the Transformer to learn the deformation field. The method learns anunsupervised generated deformation map and is tested on two benchmark datasets.In summary, our approach shows promising effectiveness in addressing stabilityand efficiency issues in image registration tasks. The source code of AiR isavailable on Github.

Attention for Image Registration (AiR): an unsupervised Transformer approach