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Proceedings Paper

A sparse coding multi-scale precise-timing machine learning algorithm for neuromorphic event-based sensors
Author(s): Germain Haessig; Ryad Benosman
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Paper Abstract

This paper introduces an unsupervised compact architecture that can extract features and classify the contents of dynamic scenes from the temporal output of a neuromorphic asynchronous event-based camera. Event-based cameras are clock-less sensors where each pixel asynchronously reports intensity changes encoded in time at the microsecond precision. While this technology is gaining more attention, there is still a lack of methodology and understanding of their temporal properties. This paper introduces an unsupervised time-oriented event-based machine learning algorithm building on the concept of hierarchy of temporal descriptors called time surfaces. In this work we show that the use of sparse coding allows for a very compact yet efficient time-based machine learning that lowers both the computational cost and memory need. We show that we can represent visual scene temporal dynamics with a finite set of elementary time surfaces while providing similar recognition rates as an uncompressed version by storing the most representative time surfaces using clustering techniques. Experiments will illustrate the main optimizations and trade-offs to consider when implementing the method for online continuous vs. offline learning. We report results on the same previously published 36 class character recognition task and a 4 class canonical dynamic card pip task, achieving 100% accuracy on each.

Paper Details

Date Published: 14 May 2018
PDF: 8 pages
Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 106391U (14 May 2018); doi: 10.1117/12.2305933
Show Author Affiliations
Germain Haessig, Sorbonne Univ., INSERM, CNRS, Institut de la Vision (France)
Ryad Benosman, Sorbonne Univ., INSERM, CNRS, Institut de la Vision (France)
Univ. of Pittsburgh Medical Ctr. (United States)
Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 10639:
Micro- and Nanotechnology Sensors, Systems, and Applications X
Thomas George; Achyut K. Dutta; M. Saif Islam, Editor(s)

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