Predicting display fit and ophthalmic fit measurements using a simulator

    公开(公告)号:US12100243B2

    公开(公告)日:2024-09-24

    申请号:US18326332

    申请日:2023-05-31

    Applicant: Google LLC

    Abstract: A system and method of detecting display fit measurements and/or ophthalmic measurements for a head mounted wearable computing device including a display device is provided. The system and method may include capturing image data including a face of a user to be fitted for the head mounted wearable computing device. A three-dimensional head pose and gaze measurements may be extracted and a three-dimensional model may be developed from the captured image data. The system may detect display fit measurements and/or ophthalmic fit measurements from the three-dimensional model, and may provide one or more head mounted wearable computing devices that meet the display fit and/or ophthalmic fit requirements.

    USING MACHINE LEARNING AND 3D PROJECTION TO GUIDE MEDICAL PROCEDURES

    公开(公告)号:US20240268897A1

    公开(公告)日:2024-08-15

    申请号:US18568938

    申请日:2022-06-13

    Abstract: A system for guiding a surgical or medical procedure includes a depth camera for acquiring images and/or video from a predetermined site of a subject before, during, or after a planned surgical or medical procedure and a high definition projector for projecting surgical markings onto this predetermined surgical site. The system also enables a remote educator or expert to guide the procedure by annotating a three-dimensional digital image of the subject such that this input is then projected onto the actual subject in real time. A trained machine learning guide generator is in electrical communication with the depth camera and the projector. Characteristically, the trained machine learning guide generator implements a trained machine learning model for the predetermined anatomic site. Advantageously, the trained machine learning guide generator is configured to control the projector using the trained machine learning model to bind projection such that surgical markings that guide surgical or medical procedures are stably projected onto the subject despite movement.

    LEARNING APPARATUS, LEARNING METHOD, TRAINED MODEL, AND PROGRAM

    公开(公告)号:US20230368880A1

    公开(公告)日:2023-11-16

    申请号:US18357143

    申请日:2023-07-23

    Inventor: Yuta HIASA

    Abstract: The learning apparatus includes a processor (129), a memory (114), and a learning model (126). The processor (129) performs processing of inputting a pseudo simple X-ray image (204), which is generated by projecting an X-ray CT image (202), to the learning model (126), processing of generating a second interpretation report (208) with respect to the pseudo simple X-ray image (204) by converting a first interpretation report (206), processing of acquiring an error between an estimation report (210) with respect to the pseudo simple X-ray image (204) output by the learning model (126) on the basis of the input pseudo simple X-ray image (204), and the second interpretation report (208), and processing of training the learning model (126) by using the error.

    SYSTEM AND METHOD FOR PROCESSING TRAINING DATASET ASSOCIATED WITH SYNTHETIC IMAGE

    公开(公告)号:US20230334831A1

    公开(公告)日:2023-10-19

    申请号:US17987452

    申请日:2022-11-15

    CPC classification number: G06V10/774 G06T17/00 G06T2210/32 G06V2201/12

    Abstract: Provided is a training dataset generating system including: a communicator to receive a two-dimensional (2D) image obtained by photographing a target object; and a controller configured to generate, based on the 2D image and based on three-dimensional (3D) data for the target object, a training dataset comprising a synthetic image and comprising labeling information, wherein the controller is configured to generate the training data set by: generating, based on the 3D data, a rendered image, generating the synthetic image, based on the 2D image and the rendered image, through deep learning training, extracting, based on at least one of the 3D data or the rendered image, the labeling information for the target object, and generating the training dataset.

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