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Proceedings Paper

An Image Understanding System Based On Macro Data Flow
Author(s): William W. Wehner
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Paper Abstract

Current algorithms used in image processing and image understanding applications impose diverse and demanding computational requirements on processor architectures. While general purpose computers can provide the needed types of computation, the performance requirements driven by high-rate imaging sensors force system architects to use highly parallel and/or specialized hardware to attain an efficient system realization. To date, end-to-end systems capable of attaining real-time performance at full sensor data rates have relied on a mixture of processor types generally including specialized hardware for low-level pixel processing and general purpose processors for abstract symbolic data manipulation. Such systems are plagued by several problems: a clean division of algorithms among processor types is difficult and can impose artificial constraints; each processor type requires a different software environment making a unified programming methodology difficult; specialized pixel processing hardware frequently possesses little or no programmability; and programmable processors rely on microcoding to maximize concurrency but make the conversion from course-grained high-order languages difficult. Honeywell's Macro architecture uses data-driven data flow techniques developed for supercomputing to attain high performance parallel computation while simplifying high-order language programming. A compact and efficient processor realization is attained through a system organization which takes advantage of the macro nature of signal and image data and through hardware constructs which make low-level pipelining invisible to the user.

Paper Details

Date Published: 27 March 1987
PDF: 8 pages
Proc. SPIE 0726, Intelligent Robots and Computer Vision V, (27 March 1987); doi: 10.1117/12.937774
Show Author Affiliations
William W. Wehner, Honeywell Systems and Research Center (United States)

Published in SPIE Proceedings Vol. 0726:
Intelligent Robots and Computer Vision V
David P. Casasent, Editor(s)

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