3D Object Recognition Using Convolutional Neural Networks with Transfer Learning Between Input Channels

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


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.


3D object recognition Transfer learning Convolutional neural networks 



This work was partially financed by FEDER funds through the “Programa Operacional Factores de Competitividade—COMPETE” and by Portuguese funds through “FCT—Fundação para a Ciência e a Tecnologia” in the framework of the project PTDC/EIA-EIA/119004/2010 and PEst-OE/EEI/LA0008/2013.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Informatics and Instituto de TelecomunicaçõesUniversity of Beira InteriorCovilhãPortugal

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