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    Dynamic, free-space user interactions for machine control
    2.
    发明授权
    Dynamic, free-space user interactions for machine control 有权

    公开(公告)号：US11243612B2

    公开(公告)日：2022-02-08

    申请号：US16195755

    申请日：2018-11-19

    Applicant: Ultrahaptics IP Two Limited

    Inventor： Raffi Bedikian ,  Jonathan Marsden ,  Keith Mertens ,  David Holz ,  Maxwell Sills ,  Matias Perez ,  Gabriel Hare ,  Ryan Julian

    IPC: G06F3/01 ,  G06F3/0481 ,  G06F3/03 ,  G06K9/00 ,  G06K9/62

    Abstract: Embodiments of display control based on dynamic user interactions generally include capturing a plurality of temporally sequential images of the user, or a body part or other control object manipulated by the user, and computationally analyzing the images to recognize a gesture performed by the user. In some embodiments, a scale indicative of an actual gesture distance traversed in performance of the gesture is identified, and a movement or action is displayed on the device based, at least in part, on a ratio between the identified scale and the scale of the displayed movement. In some embodiments, a degree of completion of the recognized gesture is determined, and the display contents are modified in accordance therewith. In some embodiments, a dominant gesture is computationally determined from among a plurality of user gestures, and an action displayed on the device is based on the dominant gesture.

    Hand initialization for machine learning based gesture recognition
    3.
    发明授权
    Hand initialization for machine learning based gesture recognition 有权

    公开(公告)号：US12260679B1

    公开(公告)日：2025-03-25

    申请号：US18391574

    申请日：2023-12-20

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06V40/20 ,  G06F3/01 ,  G06F18/21 ,  G06F18/22 ,  G06V10/44 ,  G06V30/194 ,  G06V40/10

    Abstract: The technology disclosed also initializes a new hand that enters the field of view of a gesture recognition system using a parallax detection module. The parallax detection module determines candidate regions of interest (ROI) for a given input hand image and computes depth, rotation and position information for the candidate ROI. Then, for each of the candidate ROI, an ImagePatch, which includes the hand, is extracted from the original input hand image to minimize processing of low-information pixels. Further, a hand classifier neural network is used to determine which ImagePatch most resembles a hand. For the qualified, most-hand like ImagePatch, a 3D virtual hand is initialized with depth, rotation and position matching that of the qualified ImagePatch.

    Machine learning based gesture recognition
    4.
    发明授权
    Machine learning based gesture recognition 有权

    公开(公告)号：US12229217B1

    公开(公告)日：2025-02-18

    申请号：US18536151

    申请日：2023-12-11

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06F18/214 ,  G06F3/01 ,  G06F18/24 ,  G06N3/04 ,  G06N3/08 ,  G06T7/246 ,  G06T7/285 ,  G06T7/73 ,  G06V10/70 ,  G06V20/64 ,  G06V40/20

    Abstract: The technology disclosed introduces two types of neural networks: “master” or “generalists” networks and “expert” or “specialists” networks. Both, master networks and expert networks, are fully connected neural networks that take a feature vector of an input hand image and produce a prediction of the hand pose. Master networks and expert networks differ from each other based on the data on which they are trained. In particular, master networks are trained on the entire data set. In contrast, expert networks are trained only on a subset of the entire dataset. In regards to the hand poses, master networks are trained on the input image data representing all available hand poses comprising the training data (including both real and simulated hand images).

    Hand pose estimation for machine learning based gesture recognition
    7.
    发明授权
    Hand pose estimation for machine learning based gesture recognition 有权

    公开(公告)号：US12243238B1

    公开(公告)日：2025-03-04

    申请号：US18224551

    申请日：2023-07-20

    Applicant: ULTRAHAPTICS IP TWO LIMITED

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06T7/13

    Abstract: The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by-joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.

    Hand pose estimation for machine learning based gesture recognition
    10.
    发明授权
    Hand pose estimation for machine learning based gesture recognition 有权

    公开(公告)号：US11714880B1

    公开(公告)日：2023-08-01

    申请号：US16508231

    申请日：2019-07-10

    Applicant: Ultrahaptics IP Two Limited

    Inventor： Jonathan Marsden ,  Raffi Bedikian ,  David Samuel Holz

    IPC: G06K9/62 ,  G06K9/00 ,  G06T7/13 ,  G06K9/46

    CPC classification number: G06K9/6269 ,  G06K9/00355 ,  G06K9/4604 ,  G06T7/13 ,  G06T2207/10028

    Abstract: The technology disclosed performs hand pose estimation on a so-called “joint-by-joint” basis. So, when a plurality of estimates for the 28 hand joints are received from a plurality of expert networks (and from master experts in some high-confidence scenarios), the estimates are analyzed at a joint level and a final location for each joint is calculated based on the plurality of estimates for a particular joint. This is a novel solution discovered by the technology disclosed because nothing in the field of art determines hand pose estimates at such granularity and precision. Regarding granularity and precision, because hand pose estimates are computed on a joint-by-joint basis, this allows the technology disclosed to detect in real time even the minutest and most subtle hand movements, such a bend/yaw/tilt/roll of a segment of a finger or a tilt an occluded finger, as demonstrated supra in the Experimental Results section of this application.

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