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Shape Recognition Through Tactile Contour Tracing

A Simulation Study
  • André Frank KrauseEmail author
  • Nalin Harischandra
  • Volker Dürr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9420)

Abstract

We present Contour-net, a bio-inspired model for tactile contour-tracing driven by an Hopf oscillator. By controlling the rhythmic movements of a simulated insect-like feeler, the model executes both wide searching and local sampling movements. Contour-tracing is achieved by means of contact-induced phase-forwarding of the oscillator. To classify the shape of an object, collected contact events can be directly fed into machine learning algorithms with minimal pre-processing (scaling). Three types of classifiers were evaluated, the best one being a Support Vector Machine. The likelihood of correct classification steadily increases with the number of collected contacts, enabling an incremental classification during sampling. Given a sufficiently large training data set, tactile shape recognition can be achieved in a position-, orientation- and size-invariant manner. The suitability for robotic applications is discussed.

Keywords

Tactile sensor Contour-tracing Shape recognition Artificial neural network 

Notes

Acknowledgements

This work was supported by EU grant EMICAB (FP7-ICT, grant no. 270182) to Prof. Volker Dürr. We thank Thierry Hoinville for many important suggestion regarding the model, and Holk Cruse for valuable comments on earlier versions of the manuscript.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • André Frank Krause
    • 1
    • 2
    Email author
  • Nalin Harischandra
    • 1
    • 2
  • Volker Dürr
    • 1
    • 2
  1. 1.Department of Biological CyberneticsBielefeld UniversityBielefeldGermany
  2. 2.Cognitive Interaction Technology - Centre of Excellence (CITEC)Bielefeld UniversityBielefeldGermany

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