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Proceedings Paper

Defining ATR solutions using affine transformations on a union of subspaces model
Author(s): Charles F. Hester; Kelly K. D. Risko
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Paper Abstract

The ability to recognize a target in an image is an important problem for machine vision, surveillance systems, and military weapons. There are many "solutions" to an automatic target recognition (ATR) problem proposed by practitioners. Often the definition of the problem leads to multiple solutions due to the incompleteness of the definition. Solutions are also made approximate due to resource limitations. Issues concerning "best" solution and solution performance are very open issues, since problem definitions and solutions are ill-defined. Indeed from information based physical measurement theory such as found in the Minimum Description Length (MDL) the exact solution is intractable1. Generating some clarity in defining problems on restricted sets seems an appropriate approach for improving this vagueness in ATR definitions and solutions. Given that a one to one relationship between a physical system and the MDL exists, then this uniqueness allows that a solution can be defined by its description and a norm assigned to that description. Moreover, the solution can be characterized by a set of metrics that are based on the algorithmic information of the physical measurements. The MDL, however, is not a constructive theory, but solutions can be defined by concise problem descriptions. This limits the scope of the problem and we will take this approach here. The paper will start with a definition of an ATR problem followed by our proposal of a descriptive solution using a union of subspaces model of images as described below based on Lu and Do2. This solution uses the concept of informative representations3 implicitly which we review briefly. Then we will present some metrics to be used to characterize the solution(s) which we will demonstrate by a simple example. In the discussions following the example we will suggest how this fits in the context of present and future work.

Paper Details

Date Published: 14 May 2012
PDF: 6 pages
Proc. SPIE 8391, Automatic Target Recognition XXII, 83910H (14 May 2012); doi: 10.1117/12.921297
Show Author Affiliations
Charles F. Hester, U.S. Army Research, Development , and Engineering Command (United States)
Kelly K. D. Risko, U.S. Army Research, Development , and Engineering Command (United States)


Published in SPIE Proceedings Vol. 8391:
Automatic Target Recognition XXII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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