Verizon Patent and Licensing Inc.

Oath Inc.

Jeffrey Scholz

Guy Dassa

Yale Song

Joao Vitor Baldini Soares

Minho Lee

Herve Bizira

Xingyue Zhou

Christos Papapavlou

Praveen Mareedu

Sean McCall

Zachary Tauber

Haoxin Guo

Viktor Kyriazakos

Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide an image processing framework that sub-divides computer vision techniques into three computationally efficient steps: detection, classification and matching. These steps provide an improved image processing framework that can analyze live stream data of a media file, in real-time, in order to identify and track specific digital objects depicted therein. This enables not only image processing detection results, but also the capabilities of augmenting the video stream with additional data related to the detected object.

Computer vision on broadcast video

An exemplary method includes an augmented reality system acquiring an image of an event from a viewpoint of a camera of a computing device in proximity to the event, the event including a performance area; identifying at least part of the performance area of the event within the image of the event; determining a three-dimensional (3D) pose of the performance area of the event within the image of the event; and providing, for concurrent display by a display device of the computing device, the image of the event and augmented reality content that is oriented according to the 3D pose of the performance area of the event within the image of the event.

Systems and methods for facilitating display of augmented reality content

Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel machine-in-the-loop, image-to-video bootstrapping framework that harnesses a training set built upon an image dataset and a video dataset in order to efficiently produce an accurate training set to be applied to frames of videos. The disclosed systems and methods reduce the amount of time required to build the training dataset, and also provide mechanisms to apply the training dataset to any type of content and for any type of recognition task.

Machine-in-the-loop, image-to-video computer vision bootstrapping

Growing community of inventors

New York, NY, United States of America

Jeffrey Scholz