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Proceedings Paper

Maximum entropy method applied to deblurring images on a MasPar MP-1 computer
Author(s): N. L. Bonavito; John E. Dorband; Tim Busse
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Paper Abstract

Parallel processing has been widely accepted as the approach to providing the necessary computational power to solve computer vision systems problems. Although several projects are underway to develop new architectures for computer vision, tools to effectively use those systems or commercially available multiprocessors are limited or non-existent. Unless we can develop efficient methods for mapping vision algorithms and developing programs on these architectures, the performance gains from parallel processing will be limited, and will be beyond the reach of a non-expert in parallel processing. This paper presents a design for a software development environment (SDE) for implementing vision systems applications on multiprocessors. The SDE design exploits characteristics of vision systems, and uses a classification scheme for vision algorithms to develop a parallelization and performance evaluation tool. These tools use databases that store knowledge of parallelization for different known computations on common architectures. The parallelization and performance evaluation tools use this knowledge to guide a user interactively parallelize algorithms. Some parts of SDE are currently operational and others still need to be developed.

Paper Details

Date Published: 1 April 1991
PDF: 9 pages
Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); doi: 10.1117/12.47977
Show Author Affiliations
N. L. Bonavito, NASA/Goddard Space Flight Ctr. (United States)
John E. Dorband, NASA/Goddard Space Flight Ctr. (United States)
Tim Busse, MasPar Computer Corp. (United States)


Published in SPIE Proceedings Vol. 1406:
Image Understanding in the '90s: Building Systems that Work

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