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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Human-robot interaction is an interdisciplinary research area that aims at the development of social robots. Since social robots are expected to interact with humans and understand their behavior through gestures and body movements, cognitive psychology and robot technology must be integrated. In this paper we present a biological and real-time framework for detecting and tracking hands and heads. This framework is based on keypoints extracted by means of cortical V1 end-stopped cells. Detected keypoints and the cells' responses are used to classify the junction type. Through the combination of annotated keypoints in a hierarchical, multi-scale tree structure, moving and deformable hands can be segregated and tracked over time. By using hand templates with lines and edges at only a few scales, a hand's gestures can be recognized. Head tracking and pose detection are also implemented, which can be integrated with detection of facial expressions in the future. Through the combinations of head poses and hand gestures a large number of commands can be given to a robot.

A Biological and Real-Time Framework for Hand Gestures and Head Poses