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Event-Based Visual Flow

delete2014-02-01
delete292
PRE
AI
R
Ryad Benosman *
C
Charles De Clercq
X
Xavier Lagorce
S
Sio-Hoï Ieng
C
Chiara Bartolozzi
DOI:10.1109/TNNLS.2013.2273537delete
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Abstract

Abstract

En 中文
This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. 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 to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost.
Keywords:
Event-based vision
event-based visual motion flow
neuromorphic sensors
real time
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Journal

IEEE Transactions on Neural Networks and Learning Systems cover
IEEE Transactions on Neural Networks and Learning Systems
IF:
8.9
Papers:
7.5K
Citations:
7.2W

Organization

I
istituto italiano di tecnologia - iit
Scholars:
9.1K
Papers: 6.9K
Citations: 8
S
Sorbonne Universite
Scholars:
6.2W
Papers: 4.5W
Citations: 605