Object-shape recognition from tactile images using a feed-forward neural network

Anwesha Khasnobish, Arindam Jati, Garima Singh, Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala, Eunjin Kim, Atulya K. Nagar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

The sense of touch is an extremely important sensory system in the human body which helps to understand object shape, texture, hardness in the world around us. Incorporating artificial haptic sensory systems in rehabilitative aids and in various other human computer interfaces is a thrust area of research presently. This paper presents a novel approach of shape recognition and classification from the tactile pressure images by touching the surface of various real life objects. Here four objects (viz. a planar surface, object with one edge, a cuboid i.e. object with two edges and a cylindrical object) are used for shape recognition. The obtained tactile pressure images of the object surfaces are subjected to segmentation, edge detection and a mapping procedure to finally reconstruct the particular object shapes. The reconstructed images are used as features. The processed tactile pressure images are classified with feed- forward neural network (FFNN) using extracted features. The classifier performance is tested with different signal-to-noise (SNR) ratios. Is is observed that classifier accuracy decreases with decrease in SNR, but at SNR value 6 i.e. when the noise power is one sixth of the signal power, the mean classification accuracy of the classifier is 88%. This shows the robustness of feed-forward neural network in the classification purpose. The performance of FFNN is compared with four classifiers (Linear Discriminant Analysis, Linear Support vector machine, Radial Basis Function SVM, k-Nearest Neighbor). FFNN performed best acquiring first rank with a average classification accuracy of 94.0%.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
PublisherIEEE Xplore
ISBN (Print)9781467314909
DOIs
Publication statusPublished - 30 Jul 2012
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
CountryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • edge detection
  • feed-forward neural network
  • Friedman test
  • McNemar's test
  • Shape Recognition
  • tactile image

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  • Cite this

    Khasnobish, A., Jati, A., Singh, G., Bhattacharyya, S., Konar, A., Tibarewala, D. N., Kim, E., & Nagar, A. K. (2012). Object-shape recognition from tactile images using a feed-forward neural network. In 2012 International Joint Conference on Neural Networks, IJCNN 2012 [6252593] (Proceedings of the International Joint Conference on Neural Networks). IEEE Xplore. https://doi.org/10.1109/IJCNN.2012.6252593