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3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels

Luis A. Alexandre
Department of Informatics and Instituto de Telecomunicacoes, Univ. Beira Interior, Covilha, Portugal
13th International Conference on Intelligent Autonomous Systems, Springer, 2014

@article{alexandre2014object,

   title={3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels},

   author={Alexandre, Lu{‘i}s A},

   year={2014}

}

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RGB-D data is getting ever more interest from the research community as both cheap cameras appear in the market and the applications of this type of data become more common. A current trend in processing image data is the use of convolutional neural networks (CNNs) that have consistently beat competition in most benchmark data sets. In this paper we investigate the possibility of transferring knowledge between CNNs when processing RGB-D data with the goal of both improving accuracy and reducing training time. We present experiments that show that our proposed approach can achieve both these goals.
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