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Event-Based Neuromorphic Vision for Autonomous Driving : A Paradigm Shift for Bio-Inspired Visual Sensing and Perception

Chen, Guang (författare)
Tongji Univ, Intelligent Percept & Intelligent Computat Grp, Shanghai, Peoples R China.;TUM, Munich, Germany.;Fortiss GmbH, Munich, Germany.;TUM, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany.
Cao, Hu (författare)
Tech Univ Munich, Munich, Germany.
Conradt, Jörg (författare)
KTH,Beräkningsvetenskap och beräkningsteknik (CST),Tech Univ Munich TUM, Munich, Germany.;TUM, Elite Master Program Neuroengn, Munich, Germany.
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Tang, Huajin (författare)
ASTAR, Inst Infocomm Res, Robot Cognit Lab, Singapore, Singapore.;Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China.
Rohrbein, Florian (författare)
TUM, Human Brain Project, Munich, Germany.
Knoll, Alois (författare)
TU Berlin, Dept Comp Sci, Berlin, Germany.;Tech Univ Munich TUM, Dept Informat, Munich, Germany.;TUM, Inst Comp Sci, Munich, Germany.
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Tongji Univ, Intelligent Percept & Intelligent Computat Grp, Shanghai, Peoples R China;TUM, Munich, Germany.;Fortiss GmbH, Munich, Germany.;TUM, Chair Robot Artificial Intelligence & Real Time S, Munich, Germany. Tech Univ Munich, Munich, Germany. (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2020
2020
Engelska.
Ingår i: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 37:4, s. 34-49
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • As a bio-inspired and emerging sensor, an event-based neuromorphic vision sensor has a different working principle compared to the standard frame-based cameras, which leads to promising properties of low energy consumption, low latency, high dynamic range (HDR), and high temporal resolution. It poses a paradigm shift to sense and perceive the environment by capturing local pixel-level light intensity changes and producing asynchronous event streams. Advanced technologies for the visual sensing system of autonomous vehicles from standard computer vision to event-based neuromorphic vision have been developed. In this tutorial-like article, a comprehensive review of the emerging technology is given. First, the course of the development of the neuromorphic vision sensor that is derived from the understanding of biological retina is introduced. The signal processing techniques for event noise processing and event data representation are then discussed. Next, the signal processing algorithms and applications for event-based neuromorphic vision in autonomous driving and various assistance systems are reviewed. Finally, challenges and future research directions are pointed out. It is expected that this article will serve as a starting point for new researchers and engineers in the autonomous driving field and provide a bird's-eye view to both neuromorphic vision and autonomous driving research communities.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Nyckelord

Retina
Neuromorphics
Visualization
Autonomous vehicles
Vision sensors
Silicon

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