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

Three methods are explored which help indicate whether feature points are potentially visible or occluded in the matching phase of the keyframe-based real-time visual SLAM system. The first derives a measure of potential visibility from the angular proximity to keyframes in which they were observed and globally adjusted, and preferentially selects those with high visibility when tracking the camera position between keyframes. It is found that sorting and selecting features within image bins spread over the image improves tracking stability. The second method automatically recognizes and locates 3D polyhedral objects alongside the point map, and uses them to determine occlusion. The third method uses the map points themselves to grow surfaces. The performance of each is tested on live and recorded sequences. © 2010. The copyright of this document resides with its authors

Reducing mismatching under time-pressure by reasoning about visibility and occlusion