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We report on the moving hand gesture recognition technique using Adaptive Resonance Theory (ART). To detect the start and end points of a continuous moving gesture (known as “gesture spotting” problem), we propose the adaptive distributed prediction technique. Our results show that, unlike conventional non-recurrent neural networks, the proposed technique can be utilized usefully in reliable real-time learning (2000 times faster than with alternative methods) and recognition of continuously moving patterns. [ABSTRACT FROM PUBLISHER]

In this paper we present a simple technique for real-time touchless hand gesture user interface (UI) for mobile devices based on a biologically inspired vision sensor, the dynamic vision sensor (DVS). The DVS can detect a moving object in a fast and cost effective way by outputting events asynchronously on edges of the object. The output events are spatiotemporally correlated by using novel event-driven processing algorithms based on leaky integrate-and-fire neurons to track a finger tip or to infer directions of hand swipe motions. The experimental results show that the proposed technique can achieve graphic UI capable finger tip tracking with milliseconds intervals and accurate hand swipe motion detection with negligible latency. [ABSTRACT FROM PUBLISHER]

This demonstration shows a natural gesture interface for console entertainment devices using as input a stereo pair of dynamic vision sensors. The event-based processing of the sparse sensor output allows fluid interaction at a laptop processor load of less than 3%. [ABSTRACT FROM PUBLISHER]

An algorithm to recognize four degrees of freedom gesture by using event-based image sensor is developed. The gesture motion includes three translations and one rotation. Each pixel of the event-based image sensor produces an event when temporal intensity change is larger than a pre-defined value. From the time-stamps of the events, a map of pseudo optical flow is calculated. The proposed algorithm achieves the gesture recognition based on this optical flow. It provides not only directions but also magnitudes of velocity. The proposed algorithm is memory-wise and computationally efficient because it uses only a current time-stamp map and local computation. This advantage will facilitate applications for mobile devices or on-chip development. [ABSTRACT FROM PUBLISHER]