Object Segmentation In Electrical Impedance Tomography For Tactile Sensing

Over the last decade, robotics has experienced a rapid increase in research related to human-robot interaction. Developments in artificial skin research can equip robots with tactile sensing in a similar manner to the human sense of touch. This capability will make human-robot communication more natural and safer since an important part of perception indeed relies on tactile sensing. Electrical impedance tomography (EIT)-based sensors are considered as a potentially promising alternative for tactile sensing. These sensors can reconstruct images of the conductivity variation, which appear as a response to the applied pressure. However, due to the ill-posedness of the EIT inverse problem, reconstructed images have low spatial resolution and object boundaries are not preserved. In this research, we explore the hypothesis that performing image segmentation in conjunction with preserving the object boundaries may increase the accuracy of a subsequent classification of the reconstructed images. We compare the quality of EIT images segmented by a splitting and merging segmentation algorithm, Morphological Active Contours without Edges, Random Walker and transfer learning. While the explored classical techniques appear to have predilection towards over-segmentation, the deep learning approach results to a remarkable improvement of approximately 118% in terms of the similarity index.