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  • Research Article
  • Open Access

A Statistical Detection of an Anomaly from a Few Noisy Tomographic Projections

EURASIP Journal on Advances in Signal Processing20052005:620167

https://doi.org/10.1155/ASP.2005.2215

  • Received: 1 January 2004
  • Published:

Abstract

The problem of detecting an anomaly/target from a very limited number of noisy tomographic projections is addressed from the statistical point of view. The imaged object is composed of an environment, considered as a nuisance parameter, with a possibly hidden anomaly/target. The GLR test is used to solve the problem. When the projection linearly depends on the nuisance parameters, the GLR test coincides with an optimal statistical invariant test.

Keywords and phrases

  • statistical hypotheses testing
  • (non)linear parametric model
  • nuisance parameter
  • invariant tests
  • missing observations
  • computerized tomography

Authors’ Affiliations

(1)
ISTIT, FRE CNRS 2732, Université de Technologie de Troyes, 12 rue Marie Curie, BP 2060, Troyes Cedex, 10010, France

Copyright

© Fillatre and Nikiforov 2005

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