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High-Speed Embedded-Object Analysis Using a Dual-Line Timed-Address-Event Temporal-Contrast Vision Sensor

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4 Author(s)
Belbachir, A.N. ; Safety & Security Dept., AIT Austrian Inst. of Technol., Vienna, Austria ; Hofstatter, M. ; Litzenberger, M. ; Schön, P.

This paper presents a neuromorphic dual-line vision sensor and signal-processing concepts for object recognition and classification. The system performs ultrahigh speed machine vision with a compact and low-cost embedded-processing architecture. The main innovation of this paper includes efficient edge extraction of moving objects by the vision sensor on pixel level and a novel concept for real-time embedded vision processing based on address-event data. The proposed system exploits the very high temporal resolution and the sparse visual-information representation of the event-based vision sensor. The 2 × 256 pixel dual line temporal-contrast vision sensor asynchronously responds to relative illumination-intensity changes and consequently extracts contours of moving objects. This paper shows data-volume independence from object velocity and evaluates the data quality for object velocities of up to 40 m/s (equivalent to up to 6.25 m/s on the sensor's focal plane). Subsequently, an embedded-processing concept is presented for real-time extraction of object contours and for object recognition. Finally, the influence of object velocity on high-performance embedded computer vision is discussed.

Published in:

Industrial Electronics, IEEE Transactions on  (Volume:58 ,  Issue: 3 )

Date of Publication:

March 2011

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