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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2574707
|2 doi
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|a pubmed24n0874.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Lagorce, Xavier
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a HOTS
|b A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition
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| 264 |
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|c 2017
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 31.10.2018
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| 500 |
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|a Date Revised 31.10.2018
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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.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
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a Biosensors
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| 650 |
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4 |
|a Cameras
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| 650 |
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4 |
|a Character recognition
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| 650 |
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4 |
|a Feature extraction
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| 650 |
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4 |
|a Object recognition
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| 650 |
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4 |
|a Visualization
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| 700 |
1 |
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|a Orchard, Garrick
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Galluppi, Francesco
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shi, Bertram E
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Benosman, Ryad B
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 7 vom: 01. Juli, Seite 1346-1359
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:39
|g year:2017
|g number:7
|g day:01
|g month:07
|g pages:1346-1359
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2016.2574707
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