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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 43, Iss. 2 — Jan. 10, 2004
  • pp: 315–323

Infrared target detection with probability density functions of wavelet transform subbands

Firooz A. Sadjadi  »View Author Affiliations


Applied Optics, Vol. 43, Issue 2, pp. 315-323 (2004)
http://dx.doi.org/10.1364/AO.43.000315


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Abstract

We report the development of a wavelet multiresolution texture-based algorithm that uses the probability density functions (PDFs) of the subband of the wavelet decomposition of an image. The moments of these pdfs are used in a clustering algorithm to segment the targets from their background clutter. Using the tools of experimental methodology, we evaluate the performance of this algorithm on real infrared imagery under varying algorithm parameter sets as well as scene, image, and false-alarm conditions. We estimate a set of multidimensional predictive analytic performance models that relate the detection probabilities as functions of false alarm, algorithm internal parameter, target pixel number, target-to-background interference ratio, target-interference ratio, and Fechner-Weber and local entropy metrics in the scene. These models can be used to predict performance in regions were no data are available and to optimize performance by selection of the optimum parameter and constant false-alarm values in regions with known scene and metric conditions.

© 2004 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.5010) Image processing : Pattern recognition

History
Original Manuscript: June 11, 2003
Revised Manuscript: September 1, 2003
Published: January 10, 2004

Citation
Firooz A. Sadjadi, "Infrared target detection with probability density functions of wavelet transform subbands," Appl. Opt. 43, 315-323 (2004)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-43-2-315


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