NDLI: A neuromorphic saliency-map based active vision system

Content Provider	IEEE Xplore Digital Library
Author	Sonnleithner, D. Indiveri, G.
Copyright Year	2011
Description	Author affiliation: Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, CH - 8057, Switzerland (Sonnleithner, D.; Indiveri, G.)
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.
Starting Page	1
Ending Page	6
File Size	1356992
Page Count	6
File Format	PDF
ISBN	9781424498468
e-ISBN	9781424498482
e-ISBN	9781424498475
DOI	10.1109/CISS.2011.5766145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real time systems Visualization Machine vision Sensors Workstations Voltage control Pixel
Content Type	Text
Resource Type	Article

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