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A spike-based neuromorphic stereo architecture for active vision

By Nicoletta Risi, Alessandro Aimar, Elisa Donati, Sergio Solinas and Giacomo Indiveri

Abstract

The problem of finding stereo correspondences in binocular vision is solved effortlessly in nature and yet 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 solve stereo-matching. Indeed, event-based neuromorphic hardware provides an optimal substrate for biologically-inspired, fast, asynchronous computation, that can make explicit use of precise temporal coincidences. Here we present an event-based stereo-vision system that fully leverages the advantages of brain-inspired neuromorphic computing hardware by interfacing event-based vision sensors to an event-based mixed-signal analog/digital neuromorphic processor. We describe the multi-chip sensory-processing setup developed and demonstrate a proof of concept implementation of cooperative stereo-matching that can be used to build brain-inspired active vision systems

Topics: Institute of Neuroinformatics, 570 Life sciences; biology
Publisher: s.n.
Year: 2019
DOI identifier: 10.5167/uzh-185104
OAI identifier: oai:www.zora.uzh.ch:185104
Provided by: ZORA

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