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    • 1. 发明授权
    • Dynamic hand gesture recognition using depth data
    • 使用深度数据的动态手势识别
    • US09536135B2
    • 2017-01-03
    • US13526501
    • 2012-06-18
    • Zhengyou ZhangAlexey Vladimirovich Kurakin
    • Zhengyou ZhangAlexey Vladimirovich Kurakin
    • G06K9/00
    • G06F3/017G06K9/00355G06K9/6277G06K9/6297
    • The subject disclosure is directed towards a technology by which dynamic hand gestures are recognized by processing depth data, including in real-time. In an offline stage, a classifier is trained from feature values extracted from frames of depth data that are associated with intended hand gestures. In an online stage, a feature extractor extracts feature values from sensed depth data that corresponds to an unknown hand gesture. These feature values are input to the classifier as a feature vector to receive a recognition result of the unknown hand gesture. The technology may be used in real time, and may be robust to variations in lighting, hand orientation, and the user's gesturing speed and style.
    • 主题公开涉及一种通过处理深度数据(包括实时)来识别动态手势的技术。 在离线阶段,从与预期的手势相关联的深度数据的帧中提取的特征值训练分类器。 在在线阶段,特征提取器从对应于未知手势的感测深度数据中提取特征值。 将这些特征值作为特征向量输入到分类器,以接收未知手势的识别结果。 该技术可以实时使用,并且对于照明,手取向和用户的手势速度和风格的变化可能是鲁棒的。
    • 7. 发明授权
    • Distinguishing live faces from flat surfaces
    • 将活的面孔从平坦表面区分开来
    • US08675926B2
    • 2014-03-18
    • US12796470
    • 2010-06-08
    • Zhengyou ZhangQin CaiPieter R. KasselmanArthur H. Baker
    • Zhengyou ZhangQin CaiPieter R. KasselmanArthur H. Baker
    • G06K9/00
    • G06K9/00228G06K9/00906
    • Multiple images including a face presented by a user are accessed. One or more determinations are made based on the multiple images, such as a determination of whether the face included in the multiple images is a 3-dimensional structure or a flat surface and/or a determination of whether motion is present in one or more face components (e.g., eyes or mouth). If it is determined that the face included in the multiple images is a 3-dimensional structure or that that motion is present in the one or more face components, then an indication is provided that the user can be authenticated. However, if it is determined that the face included in the multiple images is a flat surface or that motion is not present in the one or more face components, then an indication is provided that the user cannot be authenticated.
    • 访问包括用户呈现的脸部的多个图像。 基于多个图像进行一个或多个确定,例如确定包括在多个图像中的面是三维结构还是平面,和/或确定运动是否存在于一个或多个面中 组分(如眼睛或嘴巴)。 如果确定包括在多个图像中的面是三维结构或者该一个或多个面部组件中存在该运动,则提供用户可被认证的指示。 然而,如果确定包括在多个图像中的面是平面或者一个或多个面部组件中不存在运动,则提供用户不能被认证的指示。
    • 10. 发明申请
    • RECOVERING DIS-OCCLUDED AREAS USING TEMPORAL INFORMATION INTEGRATION
    • 使用时间信息整合恢复分散区域
    • US20130294710A1
    • 2013-11-07
    • US13463934
    • 2012-05-04
    • Philip Andrew ChouCha ZhangZhengyou ZhangShujie Liu
    • Philip Andrew ChouCha ZhangZhengyou ZhangShujie Liu
    • G06K9/32
    • G06K9/32G06T7/593
    • A temporal information integration dis-occlusion system and method for using historical data to reconstruct a virtual view containing an occluded area. Embodiments of the system and method use temporal information of the scene captured previously to obtain a total history. This total history is warped onto information captured by a camera at a current time in order to help reconstruct the dis-occluded areas. The historical data (or frames) from the total history match only a portion of the frames contained in the captured information. This warping yields warped history information. Warping is performed by using one of two embodiments to match points in an estimation of the current information to points in the captured information. Next, regions of current information are split using a classifier. The warped history information and the captured information then are merged to obtain an estimate for the current information and the reconstructed virtual view.
    • 一种用于使用历史数据重建包含遮挡区域的虚拟视图的时间信息整合遮挡系统和方法。 系统和方法的实施例使用先前捕获的场景的时间信息来获得总历史。 这个总历史在当前时间由相机拍摄的信息扭曲,以帮助重建被遮挡的区域。 来自总历史记录的历史数据(或帧)仅匹配捕获信息中包含的帧的一部分。 这种扭曲产生扭曲的历史信息。 通过使用两个实施例中的一个实现扭曲,以将当前信息的估计中的点与捕获的信息中的点进行匹配。 接下来,使用分类器分割当前信息的区域。 然后将翘曲的历史信息和捕获的信息合并,以获得当前信息和重建的虚拟视图的估计。