Laura  Pramparo, Robinson Jimenez  Moreno, 
    Colorimeter Using Artificial Neural Networks,
    Journal of Engineering and Applied Sciences,
    Volume 12,Issue 20,
    2017,
    Pages 5332-5337,
    ISSN 1816-949x,
    jeasci.2017.5332.5337,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2017.5332.5337)
    Abstract: The following study presents the development of a color classification algorithm for convolutional
neural networks and fully-connected neural networks which uses a database of 200 images per color and
between 12 and 18 colors to be classified for the training of the two networks. Subsequently, a comparison was
made between their accuracy percentages where the best results were 95.33% for the convolutional neural
network and 93.33% for the fully connected in the recognition of 12 colors and 93.67 and 35.23% for 18 colors,
respectively. Finally, the best network is selected to design a video recognition application and the results are
presented.
    Keywords: Convolutional neural network;fully-connected neural network;colorimeter;neural network architectures;colors