Target Identification Using Dictionary Matching of Generalized Polarization Tensors

被引：30
作者：
Ammari, Habib [1 ]
Boulier, Thomas [1 ]
Garnier, Josselin [2 ,3 ]
Jing, Wenjia [1 ]
Kang, Hyeonbae [4 ]
Wang, Han [1 ]
机构：
[1] Ecole Normale Super, Dept Math & Applicat, F-75005 Paris, France
[2] Univ Paris 07, Lab Probabil & Modeles Aleatoires, F-75205 Paris 13, France
[3] Univ Paris 07, Lab Jacques Louis Lions, F-75205 Paris 13, France
[4] Inha Univ, Dept Math, Inchon 402751, South Korea
来源：
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS | 2014年 / 14卷 / 01期
关键词：
Generalized polarization tensors; Target identification; Shape representation; Stability analysis; BOUNDARY MEASUREMENTS; IMAGE-ANALYSIS; PART I; CONDUCTIVITY; INHOMOGENEITIES; RECONSTRUCTION;
D O I：
10.1007/s10208-013-9168-6
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：27 / 62
页数：36
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