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HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition

Authors Lagorce, Xavier
Orchard, Garrick
Galluppi, Francesco
Shi, Bertram E. View this author's profile
Benosman, Ryad B.
Issue Date 2017
Source IEEE Transactions on Pattern Analysis and Machine Intelligence , v. 39, (7), July 2017, article number 7508476, p. 1346-1359
Summary This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy. © 2017 IEEE.
ISSN 0162-8828
Language English
Format Article
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