We present a fully event-driven vision and processing system for selective attention and tracking implemented on Intel's neuromorphic research chip, Loihi, directly interfaced with an event-based Dynamic Vision Sensor, DAVIS. The attention mechanism is realized as a recurrent spiking neural network (SNN) that forms sustained activation-bump attractors. The network dynamics support object tracking when distractors are present and when the object slows down or stops
The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature a...
Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in t...
Fast, localised motion detection is crucial for an efficient attention mechanism. We show that model...
We present a fully event-driven vision and processing system for selective attention and tracking, r...
This work was supported by the European Union’s ERA-NET CHIST-ERA 2018 research and innovation progr...
Computation with spiking neurons takes advantage of the abstraction of action potentials into strea...
Over the past three decades, the field of neuromorphic engineering has produced sensors and processo...
Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumptio...
Regardless of the marvels brought by the conventional frame-based cameras, they have significant dra...
Computation with spiking neurons takes advantage of the abstraction of action potentials into stream...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...
In artificial vision applications, such as tracking, a large amount of data captured by sensors is t...
Selective attention is the strategy used by biological systems to cope with the inherent limits in t...
Artificial vision systems of autonomous agents face very difficult challenges, as their vision senso...
Neuromorphic engineering takes inspiration from biology to solve engineering problems using the org...
The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature a...
Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in t...
Fast, localised motion detection is crucial for an efficient attention mechanism. We show that model...
We present a fully event-driven vision and processing system for selective attention and tracking, r...
This work was supported by the European Union’s ERA-NET CHIST-ERA 2018 research and innovation progr...
Computation with spiking neurons takes advantage of the abstraction of action potentials into strea...
Over the past three decades, the field of neuromorphic engineering has produced sensors and processo...
Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumptio...
Regardless of the marvels brought by the conventional frame-based cameras, they have significant dra...
Computation with spiking neurons takes advantage of the abstraction of action potentials into stream...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...
In artificial vision applications, such as tracking, a large amount of data captured by sensors is t...
Selective attention is the strategy used by biological systems to cope with the inherent limits in t...
Artificial vision systems of autonomous agents face very difficult challenges, as their vision senso...
Neuromorphic engineering takes inspiration from biology to solve engineering problems using the org...
The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature a...
Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in t...
Fast, localised motion detection is crucial for an efficient attention mechanism. We show that model...