Patents by Inventor Gonzalo Vaca Castano

Gonzalo Vaca Castano has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Deeply learned convolutional neural networks (CNNS) for object localization and classification
    Patent number: 10002313
    Abstract: A Convolutional Neural Network (CNN) includes an initial set of convolutional layers and max pooling units, in which any input is convoluted with the learned image filters and the output is a stack of the different filter responses. Max pooling produces a scaled version of the output. The process can be repeated several times, resulting in a stack of space invariant-scaled images. Since the operation is space invariant, the computations of these layers not need to be recomputed if interested just in certain regions of the image. A Region Of Interest (ROI) Pooling layer is used to select regions to be processed by the set of fully connected layers, which uses the response of the multiple convolutional layers of the network to determine the regions where the objects (of different scales) could be located. This object proposal method is implemented as a Region Of Interest (ROI) Selector.
    Type: Grant
    Filed: December 14, 2016
    Date of Patent: June 19, 2018
    Assignee: Sighthound, Inc.
    Inventors: Gonzalo Vaca Castano, Syed Zain Masood, Stephen Neish
  • DEEPLY LEARNED CONVOLUTIONAL NEURAL NETWORKS (CNNS) FOR OBJECT LOCALIZATION AND CLASSIFICATION
    Publication number: 20170169315
    Abstract: A Convolutional Neural Network (CNN) includes an initial set of convolutional layers and max pooling units, in which any input is convoluted with the learned image filters and the output is a stack of the different filter responses. Max pooling produces a scaled version of the output. The process can be repeated several times, resulting in a stack of space invariant-scaled images. Since the operation is space invariant, the computations of these layers not need to be recomputed if interested just in certain regions of the image. A Region Of Interest (ROI) Pooling layer is used to select regions to be processed by the set of fully connected layers, which uses the response of the multiple convolutional layers of the network to determine the regions where the objects (of different scales) could be located. This object proposal method is implemented as a Region Of Interest (ROI) Selector.
    Type: Application
    Filed: December 14, 2016
    Publication date: June 15, 2017
    Inventors: Gonzalo Vaca Castano, Syed Zain Masood, Stephen Neish