Multi-scale classification for electro-sensing
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Date
2020-06
Publication Type
Report
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yes
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Abstract
This paper introduces premier and innovative (real-time) multi-scale method for target classification in electro-sensing. The intent is that of mimicking the behavior of the weakly electric fish, which is able to retrieve much more information about the target by approaching it. The method is based on a family of transform-invariant shape descriptors computed from generalized polarization tensors (GPTs) reconstructed at multiple scales. The evidence provided by the different descriptors at each scale is fused using Dempster-Shafer Theory. Numerical simulations show that the recognition algorithm we proposed performs undoubtedly well and yields a robust classification.
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published
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Volume
2020-34
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Publisher
Seminar for Applied Mathematics, ETH Zurich
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Subject
Electro-sensing; weakly electric fish; classifier combination; shape classification; reconstruction
Organisational unit
09504 - Ammari, Habib / Ammari, Habib
Notes
Funding
172483 - Mathematics for bio-inspired imaging (SNF)
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