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Tactile Features: Recognising Touch Sensations with a Novel and Inexpensive Tactile Sensor

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Advances in Autonomous Robotics Systems (TAROS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8717))

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Abstract

A simple and cost effective new tactile sensor is presented, based on a camera capturing images of the shading of a deformable rubber membrane. In Computer Vision, the issue of information encoding and classification is well studied. In this paper we explore different ways of encoding tactile images, including: Hu moments, Zernike Moments, Principal Component Analysis (PCA), Zernike PCA, and vectorized scaling. These encodings are tested by performing tactile shape recognition using a number of supervised approaches (Nearest Neighbor, Artificial Neural Networks, Support Vector Machines, Naive Bayes). In conclusion: the most effective way of representing tactile information is achieved by combining Zernike Moments and PCA, and the most accurate classifier is Nearest Neighbor, with which the system achieves a high degree (96.4%) of accuracy at recognising seven basic shapes.

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Author information

Authors and Affiliations

  1. University of Bath, Bath, UK

    Tadeo Corradi, Peter Hall & Pejman Iravani

Authors
  1. Tadeo Corradi
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  2. Peter Hall
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  3. Pejman Iravani
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Edgbaston, B15 2TT, Birmingham, UK

    Michael Mistry  & Aleš Leonardis  & 

  2. Circuits and Systems Group, Department of Electrical and Electronic Engineering, Imperial College London, Room 913c, Exhibition Road, SW7 2BT, London, UK

    Mark Witkowski

  3. Faculty of Computing, Engineering and Mathematical Sciences, University of the West of England, Coldharbour Lane, BS16 1QY, Bristol, UK

    Chris Melhuish

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© 2014 Springer International Publishing Switzerland

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Corradi, T., Hall, P., Iravani, P. (2014). Tactile Features: Recognising Touch Sensations with a Novel and Inexpensive Tactile Sensor. In: Mistry, M., Leonardis, A., Witkowski, M., Melhuish, C. (eds) Advances in Autonomous Robotics Systems. TAROS 2014. Lecture Notes in Computer Science(), vol 8717. Springer, Cham. https://doi.org/10.1007/978-3-319-10401-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-10401-0_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10400-3

  • Online ISBN: 978-3-319-10401-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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