Implementation of jump-diffusion algorithms for understanding FLIR scenes

Aaron D. Lanterman; Michael I. Miller; Donald L. Snyder

doi:10.1117/12.213096

5 July 1995 Implementation of jump-diffusion algorithms for understanding FLIR scenes

Aaron D. Lanterman, Michael I. Miller, Donald L. Snyder

Proceedings Volume 2485, Automatic Object Recognition V; (1995) https://doi.org/10.1117/12.213096
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Aaron D. Lanterman, Michael I. Miller, and Donald L. Snyder "Implementation of jump-diffusion algorithms for understanding FLIR scenes", Proc. SPIE 2485, Automatic Object Recognition V, (5 July 1995); https://doi.org/10.1117/12.213096

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