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Event-Based Vision: A Survey
Guillermo Gallego
, Tobi Delbruck
, Garrick Orchard
, Chiara Bartolozzi
, Brian Taba
, Andrea Censi
,
Stefan Leutenegger
, Andrew J. Davison
, Jorg Conradt
, Kostas Daniilidis
, Davide Scaramuzza
Technische Universität Berlin
Einstein Center Digital Future
ETH Zurich
University of Zurich
Intel Labs
Istituto Italiano di Tecnologia
IBM Research Almaden
Imperial College London
Center for Autonomous Systems
University of Pennsylvania
Research output
:
Contribution to journal
›
Article
›
peer-review
1798
Scopus citations
Overview
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Keyphrases
Event Camera
100%
Event-based Vision
100%
High Dynamic Range
40%
Brightness Change
40%
Computer Vision
20%
Large Potential
20%
Spiking Neural Networks
20%
Learning-based
20%
Bio-inspired
20%
Robotic Vision
20%
High Dynamic
20%
Low Power Consumption
20%
High Temporal Resolution
20%
Per-pixel
20%
Feature Tracking
20%
Feature Detection
20%
Low Latency
20%
Motion Blur
20%
Optical Flow
20%
Working Principle
20%
Low Vision
20%
Frame Camera
20%
Amores
20%
Bio-inspired Sensor
20%
Fixation Rate
20%
Process Event
20%
High-speed Vision
20%
Segmentation-recognition
20%
Novel Sensor
20%
General Purpose Processor
20%
Time Location
20%
Computer Science
Neural Network
100%
Challenging Scenario
100%
Features Detection
100%
Low Power Consumption
100%
Computer Vision
100%
Temporal Resolution
100%
Engineering
Event Camera
100%
Traditional Camera
40%
Dynamic Range
40%
Computervision
20%
Low Power Consumption
20%
Image Capturing
20%
Motion Blur
20%
Features Detection
20%
Fixed Rate
20%
Temporal Resolution
20%