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公开(公告)号：US20170277942A1
公开(公告)日：2017-09-28
申请号：US15076882
申请日：2016-03-22
Applicant: Intel Corporation
Inventor： Alon Lerner , Maoz Madmony
CPC classification number: G06K9/00355 , G06F3/017 , G06K9/42 , G06K9/52 , G06K9/6215
Abstract: Identifying a local coordinate system is described for gesture recognition. In one example, a method includes receiving a gesture from a user across a horizontal axis at a depth camera, determining a horizontal vector for the user based on the received user gesture, determining a vertical vector; and determining a rotation matrix to convert positions of user gestures received by the camera to a frame of reference of the user.
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公开(公告)号：US11042732B2
公开(公告)日：2021-06-22
申请号：US16707292
申请日：2019-12-09
Applicant: Intel Corporation
Inventor： Alon Lerner , Maoz Madmony
Abstract: Identifying a local coordinate system is described for gesture recognition. In one example, a method includes receiving a gesture from a user across a horizontal axis at a depth camera, determining a horizontal vector for the user based on the received user gesture, determining a vertical vector; and determining a rotation matrix to convert positions of user gestures received by the camera to a frame of reference of the user.
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Patent Agency Ranking
公开(公告)号：US09911219B2
公开(公告)日：2018-03-06
申请号：US14749303
申请日：2015-06-24
Applicant: Intel Corporation
Inventor： Shahar Fleishman , Mark Kliger , Alon Lerner
IPC: G06K9/00 , G06T13/40 , G06F3/044 , G06T15/60 , G06T17/10 , G06T7/73 , G06T7/11 , G06T7/143 , G06T7/246 , G06T7/207 , G06K9/46 , G06K9/62 , G06F3/01 , G06F3/03
CPC classification number: G06T13/40 , G06F3/017 , G06F3/0304 , G06F3/044 , G06K9/00355 , G06K9/4604 , G06K9/6282 , G06T7/11 , G06T7/143 , G06T7/207 , G06T7/251 , G06T7/75 , G06T15/60 , G06T17/10 , G06T2200/04 , G06T2200/08 , G06T2207/10004 , G06T2207/10024 , G06T2207/10028 , G06T2207/30241 , G06T2213/08 , G06T2215/12 , G06T2219/2016
Abstract: Techniques related to pose estimation for an articulated body are discussed. Such techniques may include extracting, segmenting, classifying, and labeling blobs, generating initial kinematic parameters that provide spatial relationships of elements of a kinematic model representing an articulated body, and refining the kinematic parameters to provide a pose estimation for the articulated body.
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公开(公告)号：US09747717B2
公开(公告)日：2017-08-29
申请号：US14749295
申请日：2015-06-24
Applicant: Intel Corporation
Inventor： Shahar Fleishman , Mark Kliger , Alon Lerner
IPC: G06T13/40 , G06K9/00 , G06F3/044 , G06T15/60 , G06T17/10 , G06T7/73 , G06T7/11 , G06T7/143 , G06T7/246 , G06T7/207 , G06K9/46 , G06K9/62
CPC classification number: G06T13/40 , G06F3/017 , G06F3/0304 , G06F3/044 , G06K9/00355 , G06K9/4604 , G06K9/6282 , G06T7/11 , G06T7/143 , G06T7/207 , G06T7/251 , G06T7/75 , G06T15/60 , G06T17/10 , G06T2200/04 , G06T2200/08 , G06T2207/10004 , G06T2207/10024 , G06T2207/10028 , G06T2207/30241 , G06T2213/08 , G06T2215/12 , G06T2219/2016
Abstract: Techniques related to non-rigid transformations for articulated bodies are discussed. Such techniques may include repeatedly selecting target positions for matching a kinematic model of an articulated body, generating virtual end-effectors for the kinematic model and corresponding to the target positions, generating an inverse kinematics problem including a Jacobian matrix, and determining a change in kinematic model parameters based on the inverse kinematics problem until a convergence is attained.
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5.发明申请ITERATIVE CLOSEST POINT TECHNIQUE BASED ON A SOLUTION OF INVERSE KINEMATICS PROBLEM 有权基于反向动力学问题的解决方案的迭代闭点技术公开(公告)号：US20160335790A1
公开(公告)日：2016-11-17
申请号：US14749295
申请日：2015-06-24
Applicant: Intel Corporation
Inventor： Shahar Fleishman , Mark Kliger , Alon Lerner
CPC classification number: G06T13/40 , G06F3/017 , G06F3/0304 , G06F3/044 , G06K9/00355 , G06K9/4604 , G06K9/6282 , G06T7/11 , G06T7/143 , G06T7/207 , G06T7/251 , G06T7/75 , G06T15/60 , G06T17/10 , G06T2200/04 , G06T2200/08 , G06T2207/10004 , G06T2207/10024 , G06T2207/10028 , G06T2207/30241 , G06T2213/08 , G06T2215/12 , G06T2219/2016
Abstract: Techniques related to non-rigid transformations for articulated bodies are discussed. Such techniques may include repeatedly selecting target positions for matching a kinematic model of an articulated body, generating virtual end-effectors for the kinematic model and corresponding to the target positions, generating an inverse kinematics problem including a Jacobian matrix, and determining a change in kinematic model parameters based on the inverse kinematics problem until a convergence is attained.
Abstract translation: 讨论了关于铰接体的非刚性变换的技术。 这样的技术可以包括重复地选择目标位置以匹配铰接体的运动学模型，生成用于运动学模型的虚拟终端效应器并对应于目标位置，产生包括雅可比矩阵的逆运动学问题，以及确定运动学上的变化 基于逆运动学问题的模型参数，直到达到收敛。
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公开(公告)号：US20160335486A1
公开(公告)日：2016-11-17
申请号：US14749303
申请日：2015-06-24
Applicant: Intel Corporation
Inventor： Shahar Fleishman , Mark Kliger , Alon Lerner
CPC classification number: G06T13/40 , G06F3/017 , G06F3/0304 , G06F3/044 , G06K9/00355 , G06K9/4604 , G06K9/6282 , G06T7/11 , G06T7/143 , G06T7/207 , G06T7/251 , G06T7/75 , G06T15/60 , G06T17/10 , G06T2200/04 , G06T2200/08 , G06T2207/10004 , G06T2207/10024 , G06T2207/10028 , G06T2207/30241 , G06T2213/08 , G06T2215/12 , G06T2219/2016
Abstract: Techniques related to pose estimation for an articulated body are discussed. Such techniques may include extracting, segmenting, classifying, and labeling blobs, generating initial kinematic parameters that provide spatial relationships of elements of a kinematic model representing an articulated body, and refining the kinematic parameters to provide a pose estimation for the articulated body.
Abstract translation: 讨论了与铰接体的姿态估计有关的技术。 这样的技术可以包括提取，分割，分类和标记斑点，产生提供表示铰接体的运动学模型的元素的空间关系的初始运动学参数，以及改进运动学参数以提供铰接体的姿态估计。
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7.发明授权公开(公告)号：US10649536B2
公开(公告)日：2020-05-12
申请号：US14951370
申请日：2015-11-24
Applicant: INTEL CORPORATION
Inventor： Alon Lerner , Shahar Fleishman
IPC: G06F3/01
Abstract: Hand dimensions are determined for hand and gesture recognition with a computing interface. An input sequence of frames is received from a camera. Frames of the sequence are identified in which a hand is recognized. Points are identified in the identified frames corresponding to features of the recognized hand. A value is determined for each of a set of different feature lengths of the recognized hand using the identified points for each identified frame. Each different feature length value is collected for the identified frames independently of each other feature length value. Each different feature length value is analyzed to determine an estimate of each different feature length, and the estimated feature lengths are applied to a hand tracking system, the hand tracking system for applying commands to a computer system.
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