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Tactile approach to Material Classification - Evaluated with Human Performance

Kerr, Emmett, McGinnity, TM and Coleman, SA (2014) Tactile approach to Material Classification - Evaluated with Human Performance. In: Irish Machine Vision and Image Processing 2014. UU. 6 pp. [Conference contribution]

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Knowledge of the physical properties of objects is a requirement to enable effective robotic grasping. Identifying the material from which the object is made, is one such physical property. Characteristics of the material can be retrieved using different sensors; vision-based, tactile based or sound based. Physical contact with materials using tactile sensors can enable the retrieval of detailed information about the material, i.e. compressibility, surface texture and thermal properties. This paper describes a system to classify a wide range of materials based on their thermal properties and surface texture. This system will work towards a combined system using both tactile sensing and vision based sensing. Following acquisition of data from a sophisticated tactile sensor, the system uses principal component analysis (PCA) to extract features from the data which are used to train a two stage Artificial Neural Network (ANN) to classify materials, first into groups and then as individual materials. The system is compared with human performance and the results demonstrate that the proposed system can almost performed as effectively as humans.

Item Type:Conference contribution (Poster)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:30243
Deposited By: Dr Sonya Coleman
Deposited On:24 Sep 2014 15:33
Last Modified:24 Sep 2014 15:33

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