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    Hand initialization for machine learning based gesture recognition
    11.
    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
    12.
    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).

    Predictive information for free space gesture control and communication
    16.
    发明授权
    Predictive information for free space gesture control and communication 有权

    公开(公告)号：US11868687B2

    公开(公告)日：2024-01-09

    申请号：US18161811

    申请日：2023-01-30

    Applicant: Ultrahaptics IP Two Limited

    Inventor： David S. Holz ,  Kevin Horowitz ,  Raffi Bedikian ,  Hua Yang

    IPC: G06F17/00 ,  G06F30/20 ,  G06F3/01 ,  G06V20/64 ,  G06V40/20

    CPC classification number: G06F30/20 ,  G06F3/017 ,  G06V20/64 ,  G06V40/28

    Abstract: The technology disclosed relates to simplifying updating of a predictive model using clustering observed points. In particular, it relates to observing a set of points in 3D sensory space, determining surface normal directions from the points, clustering the points by their surface normal directions and adjacency, accessing a predictive model of a hand, refining positions of segments of the predictive model, matching the clusters of the points to the segments, and using the matched clusters to refine the positions of the matched segments. It also relates to distinguishing between alternative motions between two observed locations of a control object in a 3D sensory space by accessing first and second positions of a segment of a predictive model of a control object such that motion between the first position and the second position was at least partially occluded from observation in a 3D sensory space.

    Predictive information for free space gesture control and communication
    18.
    发明授权
    Predictive information for free space gesture control and communication 有权

    公开(公告)号：US11568105B2

    公开(公告)日：2023-01-31

    申请号：US17308903

    申请日：2021-05-05

    Applicant: Ultrahaptics IP Two Limited

    Inventor： David S. Holz ,  Kevin Horowitz ,  Raffi Bedikian ,  Hua Yang

    IPC: G06F17/10 ,  G06F30/20 ,  G06F3/01 ,  G06V20/64 ,  G06V40/20

    Abstract: The technology disclosed relates to simplifying updating of a predictive model using clustering observed points. In particular, it relates to observing a set of points in 3D sensory space, determining surface normal directions from the points, clustering the points by their surface normal directions and adjacency, accessing a predictive model of a hand, refining positions of segments of the predictive model, matching the clusters of the points to the segments, and using the matched clusters to refine the positions of the matched segments. It also relates to distinguishing between alternative motions between two observed locations of a control object in a 3D sensory space by accessing first and second positions of a segment of a predictive model of a control object such that motion between the first position and the second position was at least partially occluded from observation in a 3D sensory space.

    Hand pose estimation for machine learning based gesture recognition
    20.
    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.

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