Ultrahaptics IP Two Limited

Electronics for Imaging Gmbh

Leap Motion, Inc.

Jonathan Marsden

David S Holz

Raffi A Bedikian

Keith Mertens

Maxwell Andrew Sills

Gabriel Archacki Hare

Matias Perez

Ryan Julian

Hua Yang

Paul Durdik

Richard A Falk

Leonid Kontsevich

Dion Monstavicius

James Donald

Ramin Samadani

Paul Michel

Leon C Williams

Patrick H Wood

David Gideon Macy

Victor Kliorin

During control of a user interface via free-space motions of a hand or other suitable control object, switching between control modes can be facilitated by tracking the control object's movements relative to, and its penetration of, a virtual control construct (such as a virtual surface construct). The technology disclosed includes determining from the motion information whether a motion of the control object with respect to the virtual control construct is an engagement gesture, such as a virtual mouse click or other control device operation. The position of the virtual control construct can be updated, continuously or from time to time, based on the control object's location.

Free-space user interface and control using virtual constructs

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.

Hand initialization for machine learning based gesture recognition

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

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).

Machine learning based gesture recognition

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, the gesture is identified as an engagement gesture, and compared with reference gestures from a library of reference gestures. In some embodiments, a degree of completion of the recognized engagement 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.

Dynamic, free-space user interactions for machine control

During control of a user interface via free-space motions of a hand or other suitable control object, switching between control modes can be facilitated by tracking the control object's movements relative to, and its penetration of, a virtual control construct (such as a virtual surface construct). The position of the virtual control construct can be updated, continuously or from time to time, based on the control object's location.

The technology disclosed introduces two types of neural networks: 'master' or 'generalists' networks and 'expert' or &#x201c;specialists&#x201d; 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).

A method and system are provided for controlling a machine using gestures. The method includes sensing a variation of position of a control object using an imaging system, determining, from the variation, one or more primitives describing a characteristic of a control object moving in space, comparing the one or more primitives to one or more gesture templates in a library of gesture templates, selecting, based on a result of the comparing, one or more gesture templates corresponding to the one or more primitives, and providing at least one gesture template of the selected one or more gesture templates as an indication of a command to issue to a machine under control responsive to the variation.

Method and system for controlling a machine according to a characteristic of a control object

During control of a user interface via free-space motions of a hand or other suitable control object, switching between control modes can be facilitated by tracking the control object's movements relative to, and its contact with a 'virtual touch plane or surface' (i.e., a plane, portion of a plane, and/or surface computationally defined in space, or corresponding to any physical surface).

Systems and methods of free-space gestural interaction

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.

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San Mateo, CA, United States of America

Jonathan Marsden