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

In this paper we present a new approach for pupil segmentation. It can becomputed and trained very efficiently, making it ideal for online use for highspeed eye trackers as well as for energy saving pupil detection in mobile eyetracking. The approach is inspired by the BORE and CBF algorithms andgeneralizes the binary comparison by Haar features. Since these features areintrinsically very susceptible to noise and fluctuating light conditions, wecombine them with conditional pupil shape probabilities. In addition, we alsorank each feature according to its importance in determining the pupil shape.Another advantage of our method is the use of statistical learning, which isvery efficient and can even be used online.https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FStatsPupil&mode=list

1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning