@article{MAKHILLRJAS201813110057,
    title = {Convolutional Neural Network Training for Robotic Applications in
3D Environments},
    journal = {Research Journal of Applied Sciences},
    volume = {13},
    number = {1},
    pages = {41-46},
    year = {2018},
    issn = {1815-932x},
    doi = {rjasci.2018.41.46},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2018.41.46},
    author = {M.Robinson,S.Oscar and},
    keywords = {Convolutional neural network,robotic applications,3D environment,characteristics,dynamic,information},
    abstract = {This study presents two training schemes of three deep convolutional neural network architectures
applied to object recognition, based on the depth information supplied for a 3D camera. For this case, the depth
information allows to make the set of training images of each network, its architecture and its characteristics,
generating a dynamic recognition application by variation of the image capture point. The best scheme is
selected to add a weighting layer with saturationn for obtain a final architecture that recognize objects to
different distances with a 91.69% success that mean a maximum error of 8.31%.}
    }