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Proceedings Paper

Image understanding, visualization, registration, and data fusion of biomedical brain images
Author(s): Nahum D. Gershon; John D. Cappelletti; Stuart C. Hinds; Marcus E. Glenn
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Paper Abstract

Applying distributed memory parallel systems to image processing provides the scaleability that is required to handle large real time applications. These systems can only be used efficiently with an architecture which is able to balance processing and memory with internal and external communication bandwidth. By efficiently harnessing the power of nodal heterogeneity and communication reconfigurability, systems with an ideal balance can be assembled and applied to achieve very high levels of efficiency. This paper outlines such a Heterogeneous Distributed Memory Parallel Processor (HDMPP) and discusses the software environment that is required to take advantage of it. Meiko's Application Center has considerable experience in applying these HDMPP systems to practical applications including: high definition television, radar, sonar, medical imaging, remote sensing, machine vision, data compression, image enhancement, and pattern recognition. These examples are used to illustrate how HDMPP systems have been successfully applied and how applications have been prototyped, developed, optimized and deployed on a single commercial off the shelf product.

Paper Details

Date Published: 1 April 1991
PDF: 1 pages
Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); doi: 10.1117/12.47975
Show Author Affiliations
Nahum D. Gershon, MITRE Corp. (United States)
John D. Cappelletti, MITRE Corp. (United States)
Stuart C. Hinds, MITRE Corp. (United States)
Marcus E. Glenn, MITRE Corp. (United States)

Published in SPIE Proceedings Vol. 1406:
Image Understanding in the '90s: Building Systems that Work
Brian T. Mitchell, Editor(s)

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