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A neuromorphic approach for tracking using dynamic neural fields on a programmable vision-chip


Martel, Julien N P; Sandamirskaya, Yulia (2016). A neuromorphic approach for tracking using dynamic neural fields on a programmable vision-chip. In: 10th International Conference on Distributed Smart Camera, ICDSC 16, Paris, France, 12 September 2016 - 15 September 2016, 148-154.

Abstract

In artificial vision applications, such as tracking, a large amount of data captured by sensors is transferred to processors to extract information relevant for the task at hand. Smart vision sensors offer a means to reduce the computational burden of visual processing pipelines by placing more processing capabilities next to the sensor. In this work, we use a vision-chip in which a small processor with memory is located next to each photosensitive element. The architecture of this device is optimized to perform local operations. To perform a task like tracking, we implement a neuromorphic approach using a Dynamic Neural Field, which allows to segregate, memorize, and track objects. Our system, consisting of the vision-chip running the DNF, outputs only the activity that corresponds to the tracked objects. These outputs reduce the bandwidth needed to transfer information as well as further post-processing, since computation happens at the pixel level.

Abstract

In artificial vision applications, such as tracking, a large amount of data captured by sensors is transferred to processors to extract information relevant for the task at hand. Smart vision sensors offer a means to reduce the computational burden of visual processing pipelines by placing more processing capabilities next to the sensor. In this work, we use a vision-chip in which a small processor with memory is located next to each photosensitive element. The architecture of this device is optimized to perform local operations. To perform a task like tracking, we implement a neuromorphic approach using a Dynamic Neural Field, which allows to segregate, memorize, and track objects. Our system, consisting of the vision-chip running the DNF, outputs only the activity that corresponds to the tracked objects. These outputs reduce the bandwidth needed to transfer information as well as further post-processing, since computation happens at the pixel level.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:15 September 2016
Deposited On:27 Jan 2017 08:39
Last Modified:19 Aug 2018 07:18
Publisher:Proceedings of the 10th ACM International Conference on Distributed Smart Camera
Series Name:Proceedings of the 10th ACM International Conference on Distributed Smart Camera
Number of Pages:7
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/2967413.2967444
Official URL:http://dl.acm.org/citation.cfm?id=2967444
Project Information:
  • : FunderSNSF
  • : Grant ID205321_143947
  • : Project TitleBiological Information in Cortical Communication

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