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Applied Optics

Applied Optics


  • Vol. 41, Iss. 32 — Nov. 11, 2002
  • pp: 6858–6866

Markovian and autoregressive clutter-noise models for a pattern-recognition Wiener filter

Sovira Tan, Rupert C. D. Young, and Chris R. Chatwin  »View Author Affiliations

Applied Optics, Vol. 41, Issue 32, pp. 6858-6866 (2002)

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Most modern pattern recognition filters used in target detection require a clutter-noise estimate to perform efficiently in realistic situations. Markovian and autoregressive models are proposed as an alternative to the white-noise model that has so far been the most widely used. Simulations by use of the Wiener filter and involving real clutter scenes show that both the Markovian and the autoregressive models perform considerably better than the white-noise model. The results also show that both models are general enough to yield similar results with different types of real scenes.

© 2002 Optical Society of America

OCIS Codes
(070.0070) Fourier optics and signal processing : Fourier optics and signal processing
(070.2580) Fourier optics and signal processing : Paraxial wave optics
(070.4550) Fourier optics and signal processing : Correlators
(070.5010) Fourier optics and signal processing : Pattern recognition

Original Manuscript: April 29, 2002
Revised Manuscript: August 2, 2002
Published: November 10, 2002

Sovira Tan, Rupert C. D. Young, and Chris R. Chatwin, "Markovian and autoregressive clutter-noise models for a pattern-recognition Wiener filter," Appl. Opt. 41, 6858-6866 (2002)

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