电感知中的形状识别与分类
Shape recognition and classification in electro-sensing
AI总结：
本文通过解释弱电鱼如何利用差分成像和基于物理的分类方法，从电场扰动中提取广义极化张量并计算形状不变量，实现目标形状的识别与分类。
摘要：
本文旨在推进电感知领域的发展。它揭示了弱电鱼形状感知的物理机制。这些鱼在完全黑暗的夜晚通过其主动电定位系统进行定向。它们产生稳定的高频弱电场，并感知由附近目标引起的跨膜电位调制，该目标具有与周围水不同的导纳率。在本文中，我们解释了弱电鱼如何识别和分类目标，前提是已知目标属于某个形状集合。我们的弱电鱼模型依赖于差分成像（即通过目标引起的场扰动形成图像）和基于物理的分类。电鱼首先使用特定的位置搜索算法定位目标。然后，它可以从电场扰动中提取目标的广义（或高阶）极化张量。从提取的特征中计算刚体运动和平移下的不变量，得到形状描述符。弱电鱼可以通过将其不变量与一组学习形状的不变量进行比较来对目标进行分类。另一方面，当在多频率下进行测量时，鱼可以利用频移并使用广义极化张量的频谱内容，从而显著提高电感知分类过程对测量噪声的稳定性。令人惊讶的是，结果表明，一阶极化张量在多频率下可能足以用于分类。还讨论了一种在周围介质的介电常数可忽略的情况下消除背景场的方法，从而进一步提高分类过程的稳定性。
英文摘要：
This paper aims at advancing the field of electro-sensing. It exhibits the physical mechanism underlying shape perception for weakly electric fish. These fish orient themselves at night in complete darkness by employing their active electrolocation system. They generate a stable, high-frequency, weak electric field and perceive the transdermal potential modulations caused by a nearby target with different admittivity than the surrounding water. In this paper, we explain how weakly electric fish might identify and classify a target, knowing by advance that the latter belongs to a certain collection of shapes. Our model of the weakly electric fish relies on differential imaging, i.e., by forming an image from the perturbations of the field due to targets, and physics-based classification. The electric fish would first locate the target using a specific location search algorithm. Then it could extract, from the perturbations of the electric field, generalized (or high-order) polarization tensors of the target. Computing, from the extracted features, invariants under rigid motions and scaling yields shape descriptors. The weakly electric fish might classify a target by comparing its invariants with those of a set of learned shapes. On the other hand, when measurements are taken at multiple frequencies, the fish might exploit the shifts and use the spectral content of the generalized polarization tensors to dramatically improve the stability with respect to measurement noise of the classification procedure in electro-sensing. Surprisingly, it turns out that the first-order polarization tensor at multiple frequencies could be enough for the purpose of classification. A procedure to eliminate the background field in the case where the permittivity of the surrounding medium can be neglected, and hence improve further the stability of the classification process, is also discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
