论文标题
电敏感的多尺度分类
Multi-scale classification for electro-sensing
论文作者
论文摘要
本文介绍了电信中的目标分类的总理和创新（实时）多尺度方法。目的是模仿弱电鱼的行为，该行为能够通过接近目标来检索有关目标的更多信息。该方法基于从多个尺度重建的广义极化张量（GPT）计算出的转换不变形状描述符家族。使用Dempster-Shafer理论融合了不同描述符提供的不同描述符的证据。数值模拟表明，我们提出的识别算法无疑表现良好，并且产生了强大的分类。
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.
