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Texture- and deformability-based surface recognition by tactile image analysis

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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.

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Acknowledgments

This study has been supported by University Grants Commission (UGC), India; University with Potential for Excellence Program (UGC-UPE) (Phase II) in Cognitive Science, Jadavpur University; and Council of Scientific and Industrial Research (CSIR), India.

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Authors and Affiliations

  1. School of Bioscience and Engineering, Jadavpur University, Raja S.C. Mullick Road, Kolkata, West Bengal, 700032, India

    Anwesha Khasnobish & D. N. Tibarewala

  2. Department of Electronics and Telecommunication Engineering, Jadavpur University, Raja S.C. Mullick Road, Kolkata, West Bengal, 700032, India

    Monalisa Pal & Amit Konar

  3. Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, India

    Kunal Pal

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  1. Anwesha Khasnobish
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  2. Monalisa Pal
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  3. D. N. Tibarewala
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  4. Amit Konar
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  5. Kunal Pal
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Correspondence to Anwesha Khasnobish.

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Khasnobish, A., Pal, M., Tibarewala, D.N. et al. Texture- and deformability-based surface recognition by tactile image analysis. Med Biol Eng Comput 54, 1269–1283 (2016). https://doi.org/10.1007/s11517-016-1464-2

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