Please use this identifier to cite or link to this item: https://doi.org/10.1109/BioCAS.2013.6679698
Title: A spiking neural network architecture for visual motion estimation
Authors: Orchard, G. 
Benosman, R.
Etienne-Cummings, R.
Thakor, N.V. 
Issue Date: 2013
Citation: Orchard, G.,Benosman, R.,Etienne-Cummings, R.,Thakor, N.V. (2013). A spiking neural network architecture for visual motion estimation. 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013 : 298-301. ScholarBank@NUS Repository. https://doi.org/10.1109/BioCAS.2013.6679698
Abstract: Current interest in neuromorphic computing continues to drive development of sensors and hardware for spike-based computation. Here we describe a hierarchical architecture for visual motion estimation which uses a spiking neural network to exploit the sparse high temporal resolution data provided by neuromorphic vision sensors. Although spike-based computation differs from traditional computer vision approaches, our architecture is similar in principle to the canonical Lucas-Kanade algorithm. Output spikes from the architecture represent the direction of motion to the nearest 45 degrees, and the speed within a factor of √2 over the range 0.02 to 0.27 pixels/ms. © 2013 IEEE.
Source Title: 2013 IEEE Biomedical Circuits and Systems Conference, BioCAS 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/128773
ISBN: 9781479914715
DOI: 10.1109/BioCAS.2013.6679698
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