Target identification using dictionary matching of generalized polarization tensors - Laboratoire de Probabilités et Modèles Aléatoires
Pré-Publication, Document De Travail Année : 2014

Target identification using dictionary matching of generalized polarization tensors

Résumé

The aim of this paper is to provide a fast and efficient procedure for (real-time) target identification in imaging based on matching on a dictionary of precomputed generalized polarization tensors (GPTs). The approach is based on some important properties of the GPTs and new invariants. A new shape representation is given and numerically tested in the presence of measurement noise. The stability and resolution of the proposed identification algorithm is numerically quantified. We compare the proposed GPT-based shape representation with a moment-based one.
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Dates et versions

hal-00688241 , version 1 (03-09-2024)

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Habib Ammari, Thomas Boulier, Josselin Garnier, Wenjia Jing, Hyœnbæ Kang, et al.. Target identification using dictionary matching of generalized polarization tensors. 2012. ⟨hal-00688241⟩
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