DCNN for Tactile Sensory Data Classification based on Transfer Learning

@article{Alameh2019DCNNFT,
  title={DCNN for Tactile Sensory Data Classification based on Transfer Learning},
  author={Mohamad Gabriel Alameh and Ali Ibrahim and Maurizio Valle and Gabriele Moser},
  journal={2019 15th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)},
  year={2019},
  pages={237-240}
}
  • M. Alameh, A. Ibrahim, Gabriele Moser
  • Published 15 July 2019
  • Computer Science
  • 2019 15th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)
Tactile data processing and analysis is still essentially an open challenge. In this framework, we demonstrate a method to achieve touch modality classification using pre-trained convolutional neural networks (CNNs). The 3D tensorial tactile data generated by real human interactions on an electronic skin (E-Skin) are transformed into 2D images. Using a transfer learning approach formalized through a CNN, we address the challenging task of the recognition of the object that was touched by the E… 

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