@article{MAKHILLJEAS2017122014929,
    title = {Colorimeter Using Artificial Neural Networks},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {20},
    pages = {5332-5337},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5332.5337},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5332.5337},
    author = {Laura and},
    keywords = {Convolutional neural network,fully-connected neural network,colorimeter,neural network architectures,colors},
    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.}
    }