Patents by Inventor Anil Usumezbas

Anil Usumezbas 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).

  • Fully automated multimodal system architecture for semantic segmentation of large-scale 3D outdoor point cloud data
    Patent number: 12062174
    Abstract: A method, machine readable medium and system for semantic segmentation of 3D point cloud data includes determining ground data points of the 3D point cloud data, categorizing non-ground data points relative to a ground surface determined from the ground data points to determine legitimate non-ground data points, segmenting the determined legitimate non-ground and ground data points based on a set of common features, applying logical rules to a data structure of the features built on the segmented determined non-ground and ground data points based on their spatial relationships and incorporated within a machine learning system, and constructing a 3D semantics model from the application of the logical rules to the data structure.
    Type: Grant
    Filed: September 15, 2021
    Date of Patent: August 13, 2024
    Assignee: SRI International
    Inventors: Anil Usumezbas, Bogdan Calin Mihai Matei, Rakesh Kumar, Supun Samarasekera
  • FULLY AUTOMATED MULTIMODAL SYSTEM ARCHITECTURE FOR SEMANTIC SEGMENTATION OF LARGE-SCALE 3D OUTDOOR POINT CLOUD DATA
    Publication number: 20220222824
    Abstract: A method, machine readable medium and system for semantic segmentation of 3D point cloud data includes determining ground data points of the 3D point cloud data, categorizing non-ground data points relative to a ground surface determined from the ground data points to determine legitimate non-ground data points, segmenting the determined legitimate non-ground and ground data points based on a set of common features, applying logical rules to a data structure of the features built on the segmented determined non-ground and ground data points based on their spatial relationships and incorporated within a machine learning system, and constructing a 3D semantics model from the application of the logical rules to the data structure.
    Type: Application
    Filed: September 15, 2021
    Publication date: July 14, 2022
    Inventors: Anil Usumezbas, Bogdan Calin Mihai Matei, Rakesh Kumar, Supun Samarasekera