Asynchronous frameless event-based optical flow

Benosman, Ryad, Ieng, Sio-Hoi, Clercq, Charles, Bartolozzi, Chiara and Srinivasan, Mandyam (2012) Asynchronous frameless event-based optical flow. Neural Networks, 27 32-37. doi:10.1016/j.neunet.2011.11.001

Author Benosman, Ryad
Ieng, Sio-Hoi
Clercq, Charles
Bartolozzi, Chiara
Srinivasan, Mandyam
Title Asynchronous frameless event-based optical flow
Journal name Neural Networks   Check publisher's open access policy
ISSN 0893-6080
Publication date 2012-03
Year available 2011
Sub-type Article (original research)
DOI 10.1016/j.neunet.2011.11.001
Volume 27
Start page 32
End page 37
Total pages 6
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Collection year 2012
Language eng
Abstract This paper introduces a process to compute optical flow using an asynchronous event-based retina at high speed and low computational load. A new generation of artificial vision sensors has now started to rely on biologically inspired designs for light acquisition. Biological retinas, and their artificial counterparts, are totally asynchronous and data driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework for processing visual data using asynchronous event-based acquisition, providing a method for the evaluation of optical flow. The paper shows that current limitations of optical flow computation can be overcome by using event-based visual acquisition, where high data sparseness and high temporal resolution permit the computation of optical flow with micro-second accuracy and at very low computational cost.
Keyword Asynchronous acquisition
Temporal dynamics
Event-based vision
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online 25 November 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2012 Collection
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Citation counts: TR Web of Science Citation Count  Cited 31 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 35 times in Scopus Article | Citations
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