Paper Info
Title
High-Speed Embedded-Object Analysis Using A Dual-Line Timed-Address-Event Temporal-Contrast Vision Sensor
Abstract
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 x 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.
Year
DOI
Venue
2011
10.1109/TIE.2010.2095390
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Keywords
Field
DocType
Event-based vision, high-speed imaging, image processing, machine vision, real time
Computer vision,3D single-object recognition,Machine vision,Edge detection,Computer science,Image processing,Neuromorphic engineering,Pose,Artificial intelligence,Contextual image classification,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
58
3
0278-0046
Citations 
PageRank 
References 
2
0.42
8
Authors
4
Name
Order
Citations
PageRank
Ahmed Nabil Belbachir113517.10
Michael Hofstatter2596.86
Martin Litzenberger3547.75
Peter Schön420.42