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Proceedings Paper

Implementation of jump-diffusion algorithms for understanding FLIR scenes
Author(s): Aaron D. Lanterman; Michael I. Miller; Donald L. Snyder
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Paper Abstract

Our pattern theoretic approach to the automated understanding of forward-looking infrared (FLIR) images brings the traditionally separate endeavors of detection, tracking, and recognition together into a unified jump-diffusion process. New objects are detected and object types are recognized through discrete jump moves. Between jumps, the location and orientation of objects are estimated via continuous diffusions. An hypothesized scene, simulated from the emissive characteristics of the hypothesized scene elements, is compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over the set of possible scenes to form a posterior distribution. The jump-diffusion process empirically generates the posterior distribution. Both the diffusion and jump operations involve the simulation of a scene produced by a hypothesized configuration. Scene simulation is most effectively accomplished by pipelined rendering engines such as silicon graphics. We demonstrate the execution of our algorithm on a silicon graphics onyx/reality engine.

Paper Details

Date Published: 5 July 1995
PDF: 12 pages
Proc. SPIE 2485, Automatic Object Recognition V, (5 July 1995); doi: 10.1117/12.213096
Show Author Affiliations
Aaron D. Lanterman, Washington Univ. (United States)
Michael I. Miller, Washington Univ. (United States)
Donald L. Snyder, Washington Univ. (United States)

Published in SPIE Proceedings Vol. 2485:
Automatic Object Recognition V
Firooz A. Sadjadi, Editor(s)

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