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A neuromorphic saliency-map based active vision system


Sonnleithner, D; Indiveri, G (2011). A neuromorphic saliency-map based active vision system. In: 45th Annual Conference on Information Sciences and Systems (CISS 2011), Baltimore, MD, USA, 23 March 2011 - 25 March 2011, 1-6.

Abstract

Selective attention is a very efficient strategy for engineering active vision systems that need to extract relevant information from the scene in real-time. We propose an implementation of a saliency-map based active vision system in which Address-Event sensors and neuromorphic winner-take-all devices complement conventional imagers and machine vision components. A standard imager is mounted next to a Dynamic Vision Sensor (DVS) on a Pan-Tilt Unit. The output of the DVS is fed to an event-based Selective Attention Chip that implements a Winner-Take-All network with inhibition of return, to identify and sequentially select the most salient regions in the visual input space, and drive the Pan-Tilt Unit accordingly. We characterize the system with experiments using real-world scenarios and natural scenes, and interface it to a workstation to implement models of top-down attention used to influence the decision making process.

Selective attention is a very efficient strategy for engineering active vision systems that need to extract relevant information from the scene in real-time. We propose an implementation of a saliency-map based active vision system in which Address-Event sensors and neuromorphic winner-take-all devices complement conventional imagers and machine vision components. A standard imager is mounted next to a Dynamic Vision Sensor (DVS) on a Pan-Tilt Unit. The output of the DVS is fed to an event-based Selective Attention Chip that implements a Winner-Take-All network with inhibition of return, to identify and sequentially select the most salient regions in the visual input space, and drive the Pan-Tilt Unit accordingly. We characterize the system with experiments using real-world scenarios and natural scenes, and interface it to a workstation to implement models of top-down attention used to influence the decision making process.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:25 March 2011
Deposited On:09 Mar 2012 14:27
Last Modified:05 Apr 2016 15:43
Publisher:IEEE
Number of Pages:0
ISBN:978-1-424-49846-8
Publisher DOI:https://doi.org/10.1109/CISS.2011.5766145
Related URLs:http://ciss.jhu.edu/
Permanent URL: https://doi.org/10.5167/uzh-60765

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