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    Two-hand interaction with natural user interface
    1.
    发明授权
    Two-hand interaction with natural user interface 有权
    与自然界面的双手互动

    公开(公告)号：US09529513B2

    公开(公告)日：2016-12-27

    申请号：US13959555

    申请日：2013-08-05

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Alexandru Balan ,  Mark J. Finocchio ,  Kyungsuk David Lee

    IPC: G06F3/0481 ,  G06F3/0484 ,  G06F3/0482 ,  G06T19/00 ,  G06F3/01 ,  G06F3/03

    CPC classification number: G06F3/04847 ,  G06F3/011 ,  G06F3/017 ,  G06F3/0304 ,  G06F3/04812 ,  G06F3/04815 ,  G06F3/0482 ,  G06F3/04842 ,  G06T19/006

    Abstract: Two-handed interactions with a natural user interface are disclosed. For example, one embodiment provides a method comprising detecting via image data received by the computing device a context-setting input performed by a first hand of a user, and sending to a display a user interface positioned based on a virtual interaction coordinate system, the virtual coordinate system being positioned based upon a position of the first hand of the user. The method further includes detecting via image data received by the computing device an action input performed by a second hand of the user, the action input performed while the first hand of the user is performing the context-setting input, and sending to the display a response based on the context-setting input and an interaction between the action input and the virtual interaction coordinate system.

    Abstract translation: 公开了与自然用户界面的双手交互。 例如，一个实施例提供了一种方法，包括：通过由计算设备接收的图像数据检测由用户的第一只手执行的上下文设置输入，以及基于虚拟交互坐标系统向显示器发送定位的用户界面， 虚拟坐标系基于用户的第一只手的位置来定位。 该方法还包括通过由计算设备接收的图像数据检测由用户的第二只手执行的动作输入，当用户的第一只手执行上下文设置输入时执行的动作输入，并且向显示器发送 基于上下文设置输入的响应和动作输入与虚拟交互坐标系之间的交互。

    Near-plane segmentation using pulsed light source
    2.
    发明授权
    Near-plane segmentation using pulsed light source 有权
    使用脉冲光源的近平面分割

    公开(公告)号：US09304594B2

    公开(公告)日：2016-04-05

    申请号：US13862196

    申请日：2013-04-12

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： Mark Finocchio ,  Alexandru Balan ,  Nathan Ackerman ,  Jeffrey Margolis

    IPC: G09G5/00 ,  G06F3/048 ,  G06F3/01

    CPC classification number: G06F3/017 ,  G06F3/011

    Abstract: Methods for recognizing gestures within a near-field environment are described. In some embodiments, a mobile device, such as a head-mounted display device (HMD), may capture a first image of an environment while illuminating the environment using an IR light source with a first range (e.g., due to the exponential decay of light intensity) and capture a second image of the environment without illumination. The mobile device may generate a difference image based on the first image and the second image in order to eliminate background noise due to other sources of IR light within the environment (e.g., due to sunlight or artificial light sources). In some cases, object and gesture recognition techniques may be applied to the difference image in order to detect the performance of hand and/or finger gestures by an end user of the mobile device within a near-field environment of the mobile device.

    Abstract translation: 描述在近场环境中识别手势的方法。 在一些实施例中，诸如头戴式显示设备（HMD）的移动设备可以使用具有第一范围的IR光源（例如，由于指示衰减的指数衰减而捕获环境的第一图像，同时照亮环境 光强度），并捕获环境的第二个图像而无需照明。 移动设备可以基于第一图像和第二图像生成差异图像，以消除由于环境中的其他IR光源（例如，由于阳光或人造光源）引起的背景噪声。 在一些情况下，可以将对象和手势识别技术应用于差异图像，以便在移动设备的近场环境内检测移动设备的最终用户的手和/或手指手势的性能。

    Probabilistic and constraint based articulated model fitting
    4.
    发明授权
    Probabilistic and constraint based articulated model fitting 有权
    基于概率和约束的铰接模型拟合

    公开(公告)号：US09344707B2

    公开(公告)日：2016-05-17

    申请号：US13688064

    申请日：2012-11-28

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Kyungsuk David Lee ,  Alexandru Balan

    IPC: H04N13/02 ,  G06T17/00 ,  G06F3/01 ,  G06T19/20 ,  G06T7/00

    CPC classification number: H04N13/275 ,  G06F3/011 ,  G06F3/017 ,  G06T7/75 ,  G06T17/00 ,  G06T19/20 ,  G06T2200/04 ,  G06T2207/10028 ,  G06T2207/30196 ,  G06T2219/2016 ,  H04N13/271

    Abstract: A depth sensor obtains images of articulated portions of a user's body such as the hand. A predefined model of the articulated body portions is provided. Representative attract points of the model are matched to centroids of the depth sensor data, and a rigid transform of the model is performed, in an initial, relatively coarse matching process. This matching process is then refined in a non-rigid transform of the model, using attract point-to-centroid matching. In a further refinement, an iterative process rasterizes the model to provide depth pixels of the model, and compares the depth pixels of the model to the depth pixels of the depth sensor. The refinement is guided by whether the depth pixels of the model are overlapping or non-overlapping with the depth pixels of the depth sensor. Collision, distance and angle constraints are also imposed on the model.

    Abstract translation: 深度传感器获得诸如手的用户身体的关节部分的图像。 提供了铰接体部分的预定模型。 模型的代表性吸引点与深度传感器数据的质心相匹配，并且在初始的较粗略的匹配过程中执行模型的刚性变换。 然后，使用吸引点到质心匹配，在模型的非刚性变换中对该匹配过程进行细化。 在进一步的细化中，迭代过程光栅化模型以提供模型的深度像素，并将模型的深度像素与深度传感器的深度像素进行比较。 细化是由模型的深度像素是否与深度传感器的深度像素重叠或不重叠来指导。 对模型也施加了碰撞，距离和角度限制。

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