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Proceedings Paper

Object recognition via information: theoretic measures/metrics
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Paper Abstract

Discrimination of friendly or hostile objects is investigated using information-theory measures/metric in an image which has been compromised by a number of factors. In aerial military images, objects with different orientations can be reasonably approximated by a single identification signature consisting of the average histogram of the object under rotations. Three different information-theoretic measures/metrics are studied as possible criteria to help classify the objects. The first measure is the standard mutual information (MI) between the sampled object and the library object signatures. A second measure is based on information efficiency, which differs from MI. Finally an information distance metric is employed which determines the distance, in an information sense, between the sampled object and the library object. It is shown that the three (parsimonious) information-theoretic variables introduced here form an independent basis in the sense that any variable in the information channel can be uniquely expressed in terms of the three parameters introduced here. The methodology discussed is tested on a sample set of standardized images to evaluate their efficacy. A performance standardization methodology is presented which is based on manipulation of contrast, brightness, and size attributes of the sample objects of interest.

Paper Details

Date Published: 28 February 2007
PDF: 12 pages
Proc. SPIE 6498, Computational Imaging V, 64980Z (28 February 2007); doi: 10.1117/12.702504
Show Author Affiliations
Daniel W. Repperger, Air Force Research Lab. (United States)
Alan R. Pinkus, Air Force Research Lab. (United States)
Julie A. Skipper, Wright State Univ. (United States)
Christina D. Schrider, Wright State Univ. (United States)

Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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