Detection and classification from electromagnetic induction data

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• 作者：Habib Ammari^a ; ^{habib.ammari@ens.fr" class="auth_mail" title="E-mail the corresponding author} ; Junqing Chen^b ; ^{jqchen@math.tsinghua.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Zhiming Chen^c ; ^{zmchen@lsec.cc.ac.cn" class="auth_mail" title="E-mail the corresponding author} ; Darko Volkov^d ; ^{darko@wpi.edu" class="auth_mail" title="E-mail the corresponding author} ; Han Wang^a ; ^{han.wang@ens.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Eddy current imaging ; Induction data ; Classification ; Recognition ; Invariant shape descriptors
• 刊名：Journal of Computational Physics
• 出版年：2015
• 出版时间：15 November 2015
• 年：2015
• 卷：301
• 期：Complete
• 页码：201-217
• 全文大小：981 K

文摘

In this paper we introduce an efficient algorithm for identifying conductive objects using induction data derived from eddy currents. Our method consists of first extracting geometric features from the induction data and then matching them to precomputed data for known objects from a given dictionary. The matching step relies on fundamental properties of conductive polarization tensors and new invariance properties introduced in this paper. A new shape identification scheme is developed and tested in numerical simulations in the presence of measurement noise. Resolution and stability properties of the proposed identification algorithm are investigated.