Patents by Inventor Ariel Shlomo KELMAN

Ariel Shlomo KELMAN 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).

  • Patent number: 11113823
    Abstract: Systems and method for predicting performance of pipelines for object detection, pose estimation, and object tracking are provided. In one embodiment, a plurality of regressors are stored where each regressor may be trained for a specific object detection (OD), pose estimation (PE), and/or tracking pipeline, so that each regressor outputs a score equal to or greater than that of another regressor in response to a first feature vector as input, the first feature vector corresponding to a first object that is more suitable for the specific pipeline than other pipelines. A second feature vector is stored corresponding to a second object. The regressors are run with the second feature vector as input to derive respective scores, and a recommendation of at least one pipeline is indicated based on the derived scores with respect to the second object. OD/PE/tracking may then be performed using the at least one indicated pipeline.
    Type: Grant
    Filed: October 1, 2018
    Date of Patent: September 7, 2021
    Assignee: SEIKO EPSON CORPORATION
    Inventors: Andrei Mark Rotenstein, Asheer Kasar Bachoo, Christina Melissa Sutanto, Jennifer Jianing Sun, Ariel Shlomo Kelman
  • Publication number: 20200380723
    Abstract: A non-transitory computer readable medium storing instructions to cause one or more processors to acquire, from a camera or one or more memory storing an image data sequence captured by the camera, the image data sequence containing images of an object in a scene along a time. The instructions further cause the one or more processors to track a pose of the object through an object pose tracking algorithm and during the tracking of the pose, acquire a first pose of the object in a first image of the image data sequence. The instructions further cause the one or more processor to, during the tracking, extract two-dimensional (2D) features of the object from the first image, and store a training dataset containing the extracted 2D features and the corresponding first pose in the one or more memories or other one or more memories.
    Type: Application
    Filed: May 28, 2020
    Publication date: December 3, 2020
    Applicant: SEIKO EPSON CORPORATION
    Inventors: Dibyendu MUKHERJEE, Mirza Tahir AHMED, Ariel Shlomo KELMAN
  • Publication number: 20200105001
    Abstract: Systems and method for predicting performance of pipelines for object detection, pose estimation, and object tracking are provided. In one embodiment, a plurality of regressors are stored where each regressor may be trained for a specific object detection (OD), pose estimation (PE), and/or tracking pipeline, so that each regressor outputs a score equal to or greater than that of another regressor in response to a first feature vector as input, the first feature vector corresponding to a first object that is more suitable for the specific pipeline than other pipelines. A second feature vector is stored corresponding to a second object. The regressors are run with the second feature vector as input to derive respective scores, and a recommendation of at least one pipeline is indicated based on the derived scores with respect to the second object. OD/PE/tracking may then be performed using the at least one indicated pipeline.
    Type: Application
    Filed: October 1, 2018
    Publication date: April 2, 2020
    Applicant: SEIKO EPSON CORPORATION
    Inventors: Andrei Mark ROTENSTEIN, Asheer Kasar BACHOO, Christina Melissa SUTANTO, Jennifer Jianing SUN, Ariel Shlomo KELMAN