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Proceedings Paper

Spatial Reasoning In A Single Instruction/Multiple Data (SIMD) Architecture
Author(s): Joe R. Brown; Steven F. Venable
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Paper Abstract

This paper describes an approach to single level inferencing and spatial reasoning accomplished completely at the pixel level. The implementation of this technology is on a Geometric Arithmetic Parallel Processor (GAPP)-based machine, a single instruction/multiple data (SIMD) architecture consisting of 108 by 384 processors. Two statistical classifiers supply input images for spatial reasoning. The first input image is composed of centroids of objects and associated figures of merit (FOM), or certainty factors, for each of four object types. The second input image is composed of global regions labeled as one of six classifications, i.e., scene context. The proximity and orientation of object centroids to scene context is used to match antecedent conditions of rules that adjust the FOM of appropriate objects. For example, if an object is suspected of being a vehicle and is subsequently found to be on a road, the FOM for the vehicle is increased using an EMYCIN approach to evidentual reasoning. By using a SIMD machine, all suspected objects are rapidly processed in parallel. This approach demonstrates both the inferencing and spatial reasoning capabilities of the SIMD machine with the representation remaining at the pixel level.

Paper Details

Date Published: 29 March 1988
PDF: 4 pages
Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.947031
Show Author Affiliations
Joe R. Brown, Martin Marietta Corporation (United States)
Steven F. Venable, Martin Marietta Corporation (United States)


Published in SPIE Proceedings Vol. 0937:
Applications of Artificial Intelligence VI
Mohan M. Trivedi, Editor(s)

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