Spatial Reasoning In A Single Instruction/Multiple Data (SIMD) Architecture

Joe R. Brown; Steven F. Venable

doi:10.1117/12.947031

29 March 1988 Spatial Reasoning In A Single Instruction/Multiple Data (SIMD) Architecture

Joe R. Brown, Steven F. Venable

Proceedings Volume 0937, Applications of Artificial Intelligence VI; (1988) https://doi.org/10.1117/12.947031
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

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.

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Joe R. Brown and Steven F. Venable "Spatial Reasoning In A Single Instruction/Multiple Data (SIMD) Architecture", Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); https://doi.org/10.1117/12.947031

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KEYWORDS

Artificial intelligence

Automatic target recognition

Parallel computing

Target recognition

Roads

Forward looking infrared

Image classification

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