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Tactile features:recognising touch sensations with a novel and inexpensive tactile sensor


Reference:

Corradi, T., Hall, P. and Iravani, P., 2014. Tactile features:recognising touch sensations with a novel and inexpensive tactile sensor. In: Mistry, M., Leonardis, A., Witkowski, M. and Melhuish, C., eds. Advances in Autonomous Robotics Systems. Springer, pp. 163-172. (Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics; 8717)

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Official URL:

http://dx.doi.org/10.1007/978-3-319-10401-0_15

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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.

Details

Item Type Book Sections
CreatorsCorradi, T., Hall, P. and Iravani, P.
EditorsMistry, M., Leonardis, A., Witkowski, M. and Melhuish, C.
DOI10.1007/978-3-319-10401-0_15
Related URLs
URLURL Type
http://www.scopus.com/inward/record.url?scp=84906737738&partnerID=8YFLogxKUNSPECIFIED
DepartmentsFaculty of Engineering & Design > Mechanical Engineering
Faculty of Science > Computer Science
Research CentresMedia Technology Research Centre
Centre for Power Transmission & Motion Control
Centre for Sustainable Chemical Technologies
StatusPublished
ID Code41000

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