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

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.

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Correspondence to Lionel Fillatre.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fillatre, L., Nikiforov, I. A Statistical Detection of an Anomaly from a Few Noisy Tomographic Projections. EURASIP J. Adv. Signal Process. 2005, 620167 (2005). https://doi.org/10.1155/ASP.2005.2215

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Keywords and phrases

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