Patents by Inventor Vladimir L`vovich ARLAZAROV

Vladimir L`vovich ARLAZAROV 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).

  • Artificial intelligence using convolutional neural network with hough transform
    Patent number: 11854209
    Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.
    Type: Grant
    Filed: March 20, 2023
    Date of Patent: December 26, 2023
    Assignee: Smart Engines Service, LLC
    Inventors: Alexander Vladimirovich Sheshkus, Dmitry Petrovich Nikolaev, Vladimir L'vovich Arlazarov, Vladimir Viktorovich Arlazarov
  • ARTIFICIAL INTELLIGENCE USING CONVOLUTIONAL NEURAL NETWORK WITH HOUGH TRANSFORM
    Publication number: 20230245320
    Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.
    Type: Application
    Filed: March 20, 2023
    Publication date: August 3, 2023
    Inventors: Alexander Vladimirovich SHESHKUS, Dmitry Petrovich NIKOLAEV, Vladimir L`vovich ARLAZAROV, Vladimir Viktorovich ARLAZAROV
  • Artificial intelligence using convolutional neural network with Hough transform
    Patent number: 11636608
    Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.
    Type: Grant
    Filed: April 22, 2021
    Date of Patent: April 25, 2023
    Assignee: Smart Engines Service, LLC
    Inventors: Alexander Vladimirovich Sheshkus, Dmitry Petrovich Nikolaev, Vladimir L'vovich Arlazarov, Vladimir Viktorovich Arlazarov
  • ARTIFICIAL INTELLIGENCE USING CONVOLUTIONAL NEURAL NETWORK WITH HOUGH TRANSFORM
    Publication number: 20220122267
    Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.
    Type: Application
    Filed: April 22, 2021
    Publication date: April 21, 2022
    Inventors: Alexander Vladimirovich SHESHKUS, Dmitry Petrovich NIKOLAEV, Vladimir L`vovich ARLAZAROV, Vladimir Viktorovich ARLAZAROV