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Proceedings Paper

Parallel data fusion on a hypercube multiprocessor
Author(s): Paul B. Davis; D. Cate; Mongi A. Abidi
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Paper Abstract

A new parallel analytic data fusion method has been developed and tested on real image pairs. This fusion algorithm is based on the interaction between two analytically formulated constraints: (1) the principle of Knowledge Source Aggregation, and (2) the principle of Belief Enhancement/Withdrawal. In this paper, we discuss ways in which a message-passing multiprocessor employing the hypercube interconnection topology is exploited in order to achieve optimal speed-up in the parallel data fusion algorithm. Image parallelism is optimized by having multiple processors execute the same task but operate on different subsets of the data. Two numerical methods used to solve a system of partial differential equations resulting from the use of the Euler-Lagrange equation for the fusion process are compared. Tests conducted on an NCUBE/4 parallel computer have resulted in an effective implementation of the complete fusion process.

Paper Details

Date Published: 1 April 1991
PDF: 15 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25291
Show Author Affiliations
Paul B. Davis, Univ. of Tennessee/Knoxville (United States)
D. Cate, Texas Instruments Inc. (United States)
Mongi A. Abidi, Univ. of Tennessee/Knoxville (United States)

Published in SPIE Proceedings Vol. 1383:
Sensor Fusion III: 3D Perception and Recognition
Paul S. Schenker, Editor(s)

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