The characterisation of small conducting inclusions with low conductivities in an otherwise uniform background from low-frequency electrical field measurements has important applications in medical imaging using electrical impedance tomography as well as in geological imaging using electrical resistivity tomography. It is known that such objects can be characterised by a P\'oyla-Szeg\"o polarizability tensor. The coefficients of this tensor for different shapes and contrasts involves the solution of a transmission problem, which in general must be done numerically. With a dictionary classification algorithm in mind, we provide a series of accurate tensor characterisations of objects that can be used as benchmarks for developers of software tools for solving the transmission problem. To this end, we apply adaptive the boundary element method to achieve our accurate solutions.

翻译：低频电场测量在不统一的背景下,以低频电场测量的低导力小导体集成的特性,在利用阻电阻断断层摄影的医学成像以及利用电阻断层摄影的地质成像中具有重要的应用性。已知这些物体的特性可以用P\'oyla-Szeg\" o directive dardiction aroror 来标注。这个振幅对于不同形状和对比的系数涉及传输问题的解决,一般必须用数字来标出。我们考虑到字典分类算法,我们提供一系列精确的物体分级特性,作为软件工具开发者解决传输问题的基准。为此,我们采用边界元素法来调整我们的准确解决方案。