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Bayesian detection of random signals on random backgrounds

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

This paper describes a general approach to signal detection with uncertainty in signal and/or background distributions. Attention is restricted to binary decision problems where the hypotheses can be expressed as signal-present vs signal-absent, but otherwise the treatment is general. Many familiar results come out as special cases.

Keywords

  • Random Parameter
  • Matched Filter
  • Ideal Observer
  • Random Background
  • Multivariate Normal Model

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/3-540-63046-5_12
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References

  1. H. H. Barrett and W. Swindell: Radiological Imaging: Theory of Image Formation, Detection and Processing, Revised edition. Academic Press, San Diego, 1996

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  6. L. W. Nolte and D. Jaarsma: More on the detection of one of M orthogonal signals. J. Acoust. Soc. Am. 41, 497–505 (1967).

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  7. E. Clarkson and H. H. Barrett: Bayesian detection with amplitude, scale, orientation and position uncertainty. This volume.

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© 1997 Springer-Verlag Berlin Heidelberg

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Barrett, H.H., Abbey, C.K. (1997). Bayesian detection of random signals on random backgrounds. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_12

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  • DOI: https://doi.org/10.1007/3-540-63046-5_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63046-3

  • Online ISBN: 978-3-540-69070-2

  • eBook Packages: Springer Book Archive