ε-Optimal Anomaly Detection in Parametric Tomography

Lionel Fillatre (1) , Igor V. Nikiforov (1) , Florent Retraint (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

The paper concerns the radiographic non-destructive testing of well-manufactured objects. The detection of anomalies is addressed from the statistical point of view as a binary hypothesis testing problem with nonlinear nuisance parameters. A new detection scheme is proposed as an alternative to the classical GLR test. It is shown that this original decision rule detects anomalies with a loss of a negligible (epsiv) part of optimality with respect to an optimal invariant test designed for the "closest" hypothesis testing problem with linear nuisance parameters

Domains: Signal and Image processing

Lionel Fillatre, Igor V. Nikiforov, Florent Retraint. ε-Optimal Anomaly Detection in Parametric Tomography. 2006 IEEE International Conference on Acoustics Speed and Signal Processing, May 2006, Toulouse, France. pp.III-277-III-280, ⟨10.1109/ICASSP.2006.1660644⟩. ⟨hal-02366204⟩