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    • 1. 发明申请
    • GEODESIC SALIENCY USING BACKGROUND PRIORS
    • 使用背景技术的地球物理学
    • US20160163058A1
    • 2016-06-09
    • US14890884
    • 2013-07-31
    • Yichen WEIFang WENJian SUNMICROSOFT TECHNOLOGY LICENSING, LLC
    • Yichen WeiFang WenJian Sun
    • G06T7/00
    • G06T7/162G06K9/3233G06T7/11G06T7/136G06T7/194G06T2207/20164
    • Disclosed herein are techniques and systems for computing geodesic saliency of images using background priors. An input image may be segmented into a plurality of patches, and a graph associated with the image may be generated, the graph comprising nodes and edges. The nodes of the graph include nodes that correspond to the plurality of patches of the image plus an additional virtual background node that is added to the graph. The graph further includes edges that connect the nodes to each other, including internal edges between adjacent patches and boundary edges between those patches at the boundary of the image and the virtual background node. Using this graph, a saliency value, called the “geodesic” saliency, for each patch of the image is determined as a length of a shortest path from a respective patch to the virtual background node.
    • 这里公开了用于使用背景先验计算图像的测地学显着性的技术和系统。 可以将输入图像分割成多个片段,并且可以生成与图像相关联的图形,该图形包括节点和边缘。 图形的节点包括与图像的多个补丁相对应的节点以及添加到图形的附加虚拟背景节点。 该图进一步包括将节点彼此连接的边缘,包括相邻补丁之间的内部边缘和图像边界处的虚拟背景节点之间的这些补丁之间的边界边缘。 使用该图,对于图像的每个补丁,显着值(称为“测地线”)显着性被确定为从相应补丁到虚拟背景节点的最短路径的长度。
    • 2. 发明授权
    • Real time head pose estimation
    • 实时头部姿态估计
    • US08687880B2
    • 2014-04-01
    • US13425188
    • 2012-03-20
    • Yichen WeiFang WenJian SunTommer LeyvandJinyu LiCasey MeekhofTim Keosababian
    • Yichen WeiFang WenJian SunTommer LeyvandJinyu LiCasey MeekhofTim Keosababian
    • G06K9/62
    • G06K9/00281G06K9/00362G06K2009/4666
    • Methods are provided for generating a low dimension pose space and using the pose space to estimate one or more head rotation angles of a user head. In one example, training image frames including a test subject head are captured under a plurality of conditions. For each frame an actual head rotation angle about a rotation axis is recorded. In each frame a face image is detected and converted to an LBP feature vector. Using principal component analysis a PCA feature vector is generated. Pose classes related to rotation angles about a rotation axis are defined. The PCA feature vectors are grouped into a pose class that corresponds to the actual rotation angle associated with the PCA feature vector. Linear discriminant analysis is applied to the pose classes to generate the low dimension pose space.
    • 提供了用于产生低维度姿态空间并且使用姿态空间来估计用户头部的一个或多个头部旋转角度的方法。 在一个示例中,在多个条件下捕获包括测试对象头的训练图像帧。 对于每个帧,记录关于旋转轴的实际头部旋转角度。 在每帧中,检测到脸部图像并将其转换为LBP特征向量。 使用主成分分析生成PCA特征向量。 定义与旋转轴相关的旋转角度的姿态类。 PCA特征向量被分组为与PCA特征向量相关联的实际旋转角度对应的姿态类别。 将线性判别分析应用于姿态类以生成低维姿态空间。