A Tactile-based Fabric Learning and Classification Architecture

A. Khan , P. Maiolino , M. Khosravi , Włodzimierz Kasprzak


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.
Author A. Khan - [Università degli Studi di Genova]
A. Khan,,
, P. Maiolino - [Goldsmiths, University of London]
P. Maiolino,,
, M. Khosravi - [University of Genoa (UniGe)]
M. Khosravi,,
- Università degli studi di Genova
, Włodzimierz Kasprzak (FEIT / AK)
Włodzimierz Kasprzak,,
- The Institute of Control and Computation Engineering
Publication size in sheets0.5
Book 2016 IEEE International Conference on Information and Automation for Sustainability, 2016, IEEE
URL http://ieeexplore.ieee.org/document/7946535/
Languageen angielski
khan i in ICIafs 2016.pdf of 10-01-2018
4.1 MB
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 03-06-2020, BookChapterMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 03-06-2020, BookChapterMatConfByConferenceseries
Publication indicators WoS Citations = 0; Scopus Citations = 1
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