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Proceedings Paper

Stochastic resonance investigation of object detection in images
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Paper Abstract

Object detection in images was conducted using a nonlinear means of improving signal to noise ratio termed "stochastic resonance" (SR). In a recent United States patent application, it was shown that arbitrarily large signal to noise ratio gains could be realized when a signal detection problem is cast within the context of a SR filter. Signal-to-noise ratio measures were investigated. For a binary object recognition task (friendly versus hostile), the method was implemented by perturbing the recognition algorithm and subsequently thresholding via a computer simulation. To fairly test the efficacy of the proposed algorithm, a unique database of images has been constructed by modifying two sample library objects by adjusting their brightness, contrast and relative size via commercial software to gradually compromise their saliency to identification. The key to the use of the SR method is to produce a small perturbation in the identification algorithm and then to threshold the results, thus improving the overall system's ability to discern objects. A background discussion of the SR method is presented. A standard test is proposed in which object identification algorithms could be fairly compared against each other with respect to their relative performance.

Paper Details

Date Published: 27 February 2007
PDF: 12 pages
Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 649703 (27 February 2007); doi: 10.1117/12.702514
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. 6497:
Image Processing: Algorithms and Systems V
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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