Advances in deep learning over the last decade have led to a flurry of research in the
application of deep artificial neural networks to robotic systems, with at least 30 papers …

Researchers have recently focused their attention on vision-based hand gesture
recognition. However, due to several constraints, achieving an effective vision-driven hand …

We develop a new edge detection algorithm that addresses two critical issues in this long-
standing vision problem:(1) holistic image training; and (2) multi-scale feature learning. Our …

We present the first gesture recognition system implemented end-to-end on event-based
hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real …

Automatic detection and classification of dynamic hand gestures in real-world systems
intended for human computer interaction is challenging as: 1) there is a large diversity in …

This paper investigates the task of 2D human whole-body pose estimation, which aims to
localize dense landmarks on the entire human body including face, hands, body, and feet …

Touchless hand gesture recognition systems are becoming important in automotive user
interfaces as they improve safety and comfort. Various computer vision algorithms have …

This work develops a continuous sign language (SL) recognition framework with deep
neural networks, which directly transcribes videos of SL sentences to sequences of ordered …

This paper proposes a novel machine learning architecture, specifically designed for radio-
frequency based gesture recognition. We focus on high-frequency (60] GHz), short-range …

Sign language recognition is a challenging problem where signs are identified by
simultaneous local and global articulations of multiple sources, ie hand shape and …