Deep Learning for Surface Material Classification Using Haptic And Visual Information

Autor: Zheng, Haitian, Fang, Lu, Ji, Mengqi, Strese, Matti, Ozer, Yigitcan, Steinbach, Eckehard

When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface. More importantly, such a haptic signal is complementary to the visual appearance of t

Externí odkaz: http://arxiv.org/abs/1512.06658

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Haptic Codecs for the Tactile Internet[40pt]

Autor: Steinbach, Eckehard, Strese, Matti, Eid, Mohamad, Liu, Xun, Bhardwaj, Amit, Liu, Qian, Al-Ja'afreh, Mohammad, Mahmoodi, Toktam, Hassen, Rania, El Saddik, Abdulmotaleb, Holland, Oliver

Publikováno v: Steinbach, E, Strese, M, Eid, M, Liu, X, Bhardwaj, A, Liu, Q, Al-Ja'afreh, M, Mahmoodi, T, Hassen, R, El Saddik, A & Holland, O 2019, ' Haptic Codecs for the Tactile Internet[40pt] ', Proceedings of the IEEE, vol. 107, no. 2, 8470161, pp. 447-470 . https://doi.org/10.1109/JPROC.2018.2867835

The Tactile Internet will enable users to physically explore remote environments and to make their skills available across distances. An important technological aspect in this context is the acquisition, compression, transmission, and display of hapt

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2761::b25fc8c1fd617b65bbe9d9a60dd517f1
https://kclpure.kcl.ac.uk/ws/files/111297089/proceeding_codec.pdf

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Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder.

Autor: Ji, Mengqi, Fang, Lu, Zheng, Haitian, Strese, Matti, Steinbach, Eckehard

Publikováno v: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP); 2015, p1-6, 6p