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.