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Proceedings Paper

Issues in automatic object recognition: linking geometry and material data to predictive signature codes
Author(s): Paul H. Deitz
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Paper Abstract

The principal focus of Automatic Object Recognition (AOR) involves the generation of appropriate algorithms to process the output of multi-spectral sensor arrays. Given the high dimensionality that characterizes the signatures of targets of interest, it is normally impossible to satisfy the need for raw signature data by means of measurement records alone. Individual sensor characteristics in conjunction with aspect-angle dependence, target and background configuration (singly and in synergism), and multi-spectral tradeoffs inexorably lead to a requirement for predictive signature modeling methods. By means of this stratagem, a measured signature data base can be leveraged significantly, improving the fidelity of the overall simulation. Irrespective of the specific representation used for a three-dimensional geometry and material database, rarely does a predictive signature application code read that database directly. Rather, a specific interrogation method is used to pass particular geometric and material attributes to the application code. Clearly the nature of the physics employed in the application is both enabled and constrained by the form of the interrogation process used. In this paper, several examples of predictive radar codes are given, illustrating several strikingly different ways of linking geometry to applications. Following those examples the interface methods known to the authors will be described. While many of the techniques have already been implemented, some are currently in development. In addition, the utility of various techniques will be related to particular application codes.

Paper Details

Date Published: 1 November 1991
PDF: 17 pages
Proc. SPIE 10307, Automatic Object Recognition, 1030707 (1 November 1991); doi: 10.1117/12.2283648
Show Author Affiliations
Paul H. Deitz, U.S. Army Ballistic Research Lab. (United States)


Published in SPIE Proceedings Vol. 10307:
Automatic Object Recognition
Hatem N. Nasr, Editor(s)

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