Tracking using encoded beacons
    2.
    发明授权

    公开(公告)号:US10989800B2

    公开(公告)日:2021-04-27

    申请号:US15817811

    申请日:2017-11-20

    IPC分类号: G01S13/10 G01S5/16

    摘要: A tracking system, comprising: multiple beacons, each associated with a different cyclic equivalence class of code-word length n, and each configured to broadcast a bit-stream comprising a repeating code-word, where the code-word belongs to the associated cyclic equivalence class; and a mobile tracking unit, comprising: a sensor, and a processor, wherein the sensor is configured to simultaneously detects at least some of the bit streams, and provide each sensed bit stream in real-time to the processor, wherein for each bit-stream received by the processor from the sensor, the processor is configured to identify the beacon that broadcasted the bit-stream using the first n received bits.

    IMAGE EDITING USING LEVEL SET TREES
    4.
    发明申请
    IMAGE EDITING USING LEVEL SET TREES 有权
    图像编辑使用等级设置

    公开(公告)号:US20160117836A1

    公开(公告)日:2016-04-28

    申请号:US14918891

    申请日:2015-10-21

    IPC分类号: G06T7/00 G06T7/60 G06K9/52

    摘要: A method comprising: mapping an image onto multiple level sets connected by multiple branches, wherein each of said level sets corresponds to a predefined range of values for an attribute of said image; associating multiple pixels of said image to said multiple level sets in accordance with the value of each pixel; identifying a source level set associated with a source pixel of said multiple pixels; for each of said multiple pixels, determining that a distance between said source pixel and one of said multiple pixels is within a predefined threshold, wherein said distance is calculated as a function of a first distance between said source level set and said level set associated with said one of said multiple pixels; in one embodiment, the determined distance is applied to an image processing application to produce a processed image, and the processed image is rendered on a rendering medium.

    摘要翻译: 一种方法,包括:将图像映射到由多个分支连接的多个级别集上,其中每个所述级别集合对应于所述图像的属性的预定范围的值; 根据每个像素的值将所述图像的多个像素与所述多个等级集相关联; 识别与所述多个像素的源像素相关联的源级设置; 对于所述多个像素中的每一个,确定所述源像素与所述多个像素中的一个像素之间的距离在预定阈值之内,其中所述距离被计算为所述源电平集与所述多个像素相关联的所述电平集之间的第一距离的函数 所述多个像素中的一个; 在一个实施例中,将确定的距离应用于图像处理应用以产生经处理的图像,并且将处理的图像呈现在再现介质上。

    Image editing using level set trees
    6.
    发明授权
    Image editing using level set trees 有权
    使用级别设置树的图像编辑

    公开(公告)号:US09471991B2

    公开(公告)日:2016-10-18

    申请号:US14918891

    申请日:2015-10-21

    摘要: A method comprising: mapping an image onto multiple level sets connected by multiple branches, wherein each of said level sets corresponds to a predefined range of values for an attribute of said image; associating multiple pixels of said image to said multiple level sets in accordance with the value of each pixel; identifying a source level set associated with a source pixel of said multiple pixels; for each of said multiple pixels, determining that a distance between said source pixel and one of said multiple pixels is within a predefined threshold, wherein said distance is calculated as a function of a first distance between said source level set and said level set associated with said one of said multiple pixels; in one embodiment, the determined distance is applied to an image processing application to produce a processed image, and the processed image is rendered on a rendering medium.

    摘要翻译: 一种方法,包括:将图像映射到由多个分支连接的多个级别集上,其中每个所述级别集合对应于所述图像的属性的预定范围的值; 根据每个像素的值将所述图像的多个像素与所述多个等级集相关联; 识别与所述多个像素的源像素相关联的源级设置; 对于所述多个像素中的每一个,确定所述源像素与所述多个像素中的一个像素之间的距离在预定阈值之内,其中所述距离被计算为所述源电平集与所述多个像素相关联的所述电平集之间的第一距离的函数 所述多个像素中的一个; 在一个实施例中,将确定的距离应用于图像处理应用以产生经处理的图像,并且将处理的图像呈现在再现介质上。

    METHOD AND SYSTEM FOR PRINCIPAL COMPONENT ANALYSIS
    7.
    发明申请
    METHOD AND SYSTEM FOR PRINCIPAL COMPONENT ANALYSIS 审中-公开
    主成分分析方法与系统

    公开(公告)号:US20150074158A1

    公开(公告)日:2015-03-12

    申请号:US14480747

    申请日:2014-09-09

    IPC分类号: G06F1/02 G06F17/16

    CPC分类号: G06K9/00214

    摘要: A method of constructing a set of basis functions is disclosed. The method comprises: receiving a set of data vectors describing a physical object or a physical phenomenon; using a data processor for calculating a set of eigenvalues for an objective matrix defined as a sum of a first matrix corresponding to the set of data vectors and a second matrix corresponding to a Laplace-Beltrami operator, the objective matrix being a positive definite matrix; and constructing the set of basis functions based on at least a subset of the eigenvalues.

    摘要翻译: 公开了一种构建一组基函数的方法。 该方法包括:接收描述物理对象或物理现象的一组数据矢量; 使用数据处理器来计算用于定义为与所述数据矢量集合相对应的第一矩阵和对应于拉普拉斯 - 贝尔特拉米算子的第二矩阵的和的目标矩阵的特征值集合,所述目标矩阵是正定矩阵; 以及基于所述特征值的至少一个子集来构建所述一组基函数。

    Gesture recognition using multi-sensory data

    公开(公告)号:US10963041B2

    公开(公告)日:2021-03-30

    申请号:US16795980

    申请日:2020-02-20

    摘要: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

    Training ensembles of randomized decision trees
    9.
    发明授权
    Training ensembles of randomized decision trees 有权
    训练随机决策树组合

    公开(公告)号:US09324040B2

    公开(公告)日:2016-04-26

    申请号:US14168035

    申请日:2014-01-30

    IPC分类号: G06F15/18 G06N99/00

    CPC分类号: G06N99/005

    摘要: A method training a randomized decision tree through multiple iterations, each is based on: a) Receiving multiple data samples that include data subsets, each data subset corresponds to an attribute. b) Distributing the data subsets to slave processing units after sorting the data samples in consecutive ascending order by updating a first index that identifies trajectories of the training data samples through the tree nodes of the previous tree level. c) Simultaneously processing the data subsets to identify split functions for each tree node with respect to each data subset and updating a second index that identifies the trajectories of the training data samples through the tree node of the current tree level. d) Collecting the split functions from the slave processing units and constructing the current tree level by selecting a preferred split function for each tree node of the current tree level.

    摘要翻译: 一种通过多次迭代训练随机决策树的方法,每种方法都基于:a)接收包括数据子集的多个数据样本,每个数据子集对应一个属性。 b)通过更新通过先前树级别的树节点来标识训练数据样本的轨迹的第一索引,以连续的升序排列数据样本之后,将数据子集分配给从属处理单元。 c)同时处理数据子集以识别每个树节点相对于每个数据子集的分割函数,并且通过当前树级别的树节点更新识别训练数据样本的轨迹的第二索引。 d)从从属处理单元收集分割函数并通过为当前树级别的每个树节点选择一个优选的分割函数来构建当前树级别。