Visual marker
    1.
    发明授权

    公开(公告)号:US12093763B2

    公开(公告)日:2024-09-17

    申请号:US18010578

    申请日:2021-06-15

    Applicant: APPLE INC.

    CPC classification number: G06K19/06168

    Abstract: Various implementations disclosed herein include devices, systems, and methods that provide a visual marker including a plurality of markings arranged in a corresponding plurality of shapes. In some implementations, each marking is formed of a set of sub-markings separated by gaps and arranged according to a respective shape, and the gaps of the plurality of markings are configured to encode data and indicate orientation of the visual marker. In some implementations, the plurality of markings are arranged in a plurality of concentric rings of increasing size. In some implementations, the orientation is encoded in a first set of gaps and data in a second set of gaps of the gaps in the plurality of markings.

    VISUAL MARKER
    2.
    发明申请

    公开(公告)号:US20240403590A1

    公开(公告)日:2024-12-05

    申请号:US18802129

    申请日:2024-08-13

    Applicant: APPLE INC.

    Abstract: Various implementations disclosed herein include devices, systems, and methods that provide a visual marker including a plurality of markings arranged in a corresponding plurality of shapes. In some implementations, each marking is formed of a set of sub-markings separated by gaps and arranged according to a respective shape, and the gaps of the plurality of markings are configured to encode data and indicate orientation of the visual marker. In some implementations, the plurality of markings are arranged in a plurality of concentric rings of increasing size. In some implementations, the orientation is encoded in a first set of gaps and data in a second set of gaps of the gaps in the plurality of markings.

    Robust blur estimation for shapes with known structural elements

    公开(公告)号:US12148198B1

    公开(公告)日:2024-11-19

    申请号:US17719553

    申请日:2022-04-13

    Applicant: Apple Inc.

    Abstract: Various implementations disclosed herein assess the blurriness of portions of images depicting shapes such as codes or text that have known structural elements. This may involve determining whether a portion of an image of a code or text is sufficiently clear (not blurry) to be accurately interpreted. Blur may be assessed based on spatial frequency of statistical analysis. Blur may be assessed using a machine learning model that is trained using target blur metrics determined based on spatial frequency (e.g., analysis of high frequency portions of discrete cosine transforms of image portions) or statistical analysis (e.g., based on corner/edge detection in image portions).

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