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 Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper describes an unsupervised machine learning methodology capable oftarget tracking and background suppression via a novel dual-model approach.``Jekyll`` produces a video bit-mask describing an estimate of the locations ofmoving objects, and ``Hyde`` outputs a pseudo-background frame to subtract fromthe original input image sequence. These models were trained with acustom-modified version of Cross Entropy Loss.  Simulated data were used to compare the performance of Jekyll and Hydeagainst a more traditional supervised Machine Learning approach. The resultsfrom these comparisons show that the unsupervised methods developed arecompetitive in output quality with supervised techniques, without theassociated cost of acquiring labeled training data.

Unsupervised Learning for Target Tracking and Background Subtraction in Satellite Imagery