A Tactile-based Fabric Learning and Classification Architecture

A.A. Khan, M. Khosravi, S. Denei, P. Maiolino, W. Kasprzak, F. Mastrogiovanni, G. Cannata. IEEE ICiAfs 2016

In collaboration with University of Genova

Abstract This paper proposes an architecture for tactile-based fabric learning and classification. The architecture is based on a number of SVM-based learning units, which we call fabric classification cores, specifically trained to discriminate between two fabrics. Each core is based on a specific subset of the fully available set of features, on the basis of their discriminative value, determined using the p-value. During fabric recognition, each core casts a vote. The architecture collects votes and provides an overall classification result. We tested seventeen different fabrics, and the result showed that classification errors are negligible.