Texture- and deformability-based surface recognition by tactile image analysis View Full Text


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Article Info

DATE

2016-08

AUTHORS

Anwesha Khasnobish, Monalisa Pal, D. N. Tibarewala, Amit Konar, Kunal Pal

ABSTRACT

Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture. More... »

PAGES

1269-1283

References to SciGraph publications

  • 2007-01. Study of discriminant analysis applied to motor imagery bipolar data in MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • 2008-02. Spike sorting based on multi-class support vector machine with superposition resolution in MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • 2011-10. Trends in rehabilitation robotics in MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • 2011-01. Engineering and ethical constraints in MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • 2014-04. Object-shape recognition and 3D reconstruction from tactile sensor images in MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11517-016-1464-2

    DOI

    http://dx.doi.org/10.1007/s11517-016-1464-2

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27008211


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    46 schema:description Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture.
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