The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature and yet it is still a critical bottleneck for artificial machine vision systems. As temporal information is a crucial feature in this process, the advent of event-based vision sensors and dedicated event-based processors promises to offer an effective approach to solving the stereo matching problem. Indeed, event-based neuromorphic hardware provides an optimal substrate for fast, asynchronous computation, that can make explicit use of precise temporal coincidences. However, although several biologically-inspired solutions have already been proposed, the performance benefits of combining event-based sensing with asynchronous and parallel computation are yet to be explored. Here we present a hardware spike-based stereo-vision system that leverages the advantages of brain-inspired neuromorphic computing by interfacing two event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor. We describe a prototype interface designed to enable the emulation of a stereo-vision system on neuromorphic hardware and we quantify the stereo matching performance with two datasets. Our results provide a path toward the realization of low-latency, end-to-end event-based, neuromorphic architectures for stereo vision.

A Spike-Based Neuromorphic Architecture of Stereo Vision / Risi, N.; Aimar, A.; Donati, E.; Solinas, S.; Indiveri, G.. - In: FRONTIERS IN NEUROROBOTICS. - ISSN 1662-5218. - 14:(2020), p. 568283. [10.3389/fnbot.2020.568283]

A Spike-Based Neuromorphic Architecture of Stereo Vision

Solinas S.
Investigation
;
2020

Abstract

The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature and yet it is still a critical bottleneck for artificial machine vision systems. As temporal information is a crucial feature in this process, the advent of event-based vision sensors and dedicated event-based processors promises to offer an effective approach to solving the stereo matching problem. Indeed, event-based neuromorphic hardware provides an optimal substrate for fast, asynchronous computation, that can make explicit use of precise temporal coincidences. However, although several biologically-inspired solutions have already been proposed, the performance benefits of combining event-based sensing with asynchronous and parallel computation are yet to be explored. Here we present a hardware spike-based stereo-vision system that leverages the advantages of brain-inspired neuromorphic computing by interfacing two event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor. We describe a prototype interface designed to enable the emulation of a stereo-vision system on neuromorphic hardware and we quantify the stereo matching performance with two datasets. Our results provide a path toward the realization of low-latency, end-to-end event-based, neuromorphic architectures for stereo vision.
A Spike-Based Neuromorphic Architecture of Stereo Vision / Risi, N.; Aimar, A.; Donati, E.; Solinas, S.; Indiveri, G.. - In: FRONTIERS IN NEUROROBOTICS. - ISSN 1662-5218. - 14:(2020), p. 568283. [10.3389/fnbot.2020.568283]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/248699
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