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    • 12. 发明授权
    • Object tracking in video with visual constraints
    • 视频约束对象跟踪
    • US08477998B1
    • 2013-07-02
    • US13309999
    • 2011-12-02
    • Minyoung KimSanjiv KumarHenry A. Rowley
    • Minyoung KimSanjiv KumarHenry A. Rowley
    • G06K9/00
    • G06K9/00261G06K9/6214G06K9/6264G06K9/6277
    • Embodiments of the present invention relate to object tracking in video. In an embodiment, a computer-implemented method tracks an object in a frame of a video. An adaptive term value is determined based on an adaptive model and at least a portion of the frame. A pose constraint value is determined based on a pose model and at least a portion the frame. An alignment confidence score is determined based on an alignment model and at least a portion the frame. Based on the adaptive term value, the pose constraint value, and the alignment confidence score, an energy value is determined. Based on the energy value, a resultant tracking state is determined. The resultant tracking state defines a likely position of the object in the frame given the object's likely position in a set of previous frames in the video.
    • 本发明的实施例涉及视频中的对象跟踪。 在一个实施例中,计算机实现的方法跟踪视频帧中的对象。 基于自适应模型和帧的至少一部分来确定自适应项值。 基于姿态模型和帧的至少一部分来确定姿势约束值。 基于对准模型和框架的至少一部分来确定对准置信度得分。 基于自适应项值,姿态约束值和对准置信度得分,确定能量值。 基于能量值,确定合成的跟踪状态。 所得到的跟踪状态定义了给定对象在视频中的一组先前帧中的可能位置的帧中的对象的可能位置。
    • 16. 发明申请
    • ANNOTATING IMAGES
    • 提示图像
    • US20090304272A1
    • 2009-12-10
    • US12425910
    • 2009-04-17
    • Ameesh MakadiaSanjiv Kumar
    • Ameesh MakadiaSanjiv Kumar
    • G06K9/68G06K9/00
    • G06F17/241G06F17/30265G06K9/00664G06K9/46
    • Methods, systems, and apparatus, including computer program products, for generating data for annotating images automatically. In one aspect, a method includes receiving an input image, identifying one or more nearest neighbor images of the input image from among a collection of images, in which each of the one or more nearest neighbor images is associated with a respective one or more image labels, assigning a plurality of image labels to the input image, in which the plurality of image labels are selected from the image labels associated with the one or more nearest neighbor images, and storing in a data repository the input image having the assigned plurality of image labels. In another aspect, a method includes assigning a single image label to the input image, in which the single image label is selected from labels associated with multiple ranked nearest neighbor images.
    • 方法,系统和装置,包括计算机程序产品,用于自动生成用于注释图像的数据。 一方面,一种方法包括接收输入图像,从图像集合中识别输入图像的一个或多个最近邻图像,其中所述一个或多个最近邻图像中的每一个与相应的一个或多个图像相关联 标签,将多个图像标签分配给输入图像,其中从与一个或多个最近邻图像相关联的图像标签中选择多个图像标签,并且在数据存储库中存储具有分配的多个图像标签的输入图像 图像标签。 在另一方面,一种方法包括向输入图像分配单个图像标签,其中从与多个排序的最邻近图像相关联的标签中选择单个图像标签。
    • 17. 发明授权
    • Semi-supervised and unsupervised generation of hash functions
    • 半监督和无监督的哈希函数生成
    • US08924339B1
    • 2014-12-30
    • US13183939
    • 2011-07-15
    • Sanjiv KumarJun Wang
    • Sanjiv KumarJun Wang
    • G06F17/00G06N7/04
    • G06N99/005H04L9/3236
    • Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating hash functions. In one aspect, a method includes generating hash functions by sequentially determining a weight vector for each hash function to maximize an accuracy measure derived from current constraint weights and updating the current constraint weights for use in calculating a weight vector of a next hash function in the sequence. In another aspect, the determined weight vector maximizes an accuracy measure and a variance measure. In still another aspect, a method includes generating an adjusted covariance matrix and generating a sequence of hash functions from the adjusted covariance matrix. In still another aspect, a method includes sequentially generating a sequence of hash functions, where the weight vectors for any previously generated hash functions are used to identify constraints used to generate the weight vector for each next hash function in the sequence.
    • 方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于生成散列函数。 一方面,一种方法包括:通过依次确定每个散列函数的加权向量来产生哈希函数,以使从当前约束权重导出的精度度量最大化并更新当前约束权重,以用于计算下一个哈希函数的权重向量 序列。 在另一方面,确定的权重向量使精度测量和方差测量最大化。 在另一方面,一种方法包括生成经调整的协方差矩阵,并从调整的协方差矩阵生成散列函数序列。 在另一方面,一种方法包括依次生成散列函数序列,其中用于任何先前生成的散列函数的加权向量用于识别用于生成序列中每个下一个散列函数的加权向量的约束。
    • 18. 发明授权
    • Semi-supervised and unsupervised generation of hash functions
    • 半监督和无监督的哈希函数生成
    • US08510236B1
    • 2013-08-13
    • US13103992
    • 2011-05-09
    • Sanjiv KumarJun Wang
    • Sanjiv KumarJun Wang
    • G06F15/18
    • G06N99/005H04L9/3236
    • Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating hash functions. In one aspect, a method includes generating hash functions by sequentially determining a weight vector for each hash function to maximize an accuracy measure derived from current constraint weights and updating the current constraint weights for use in calculating a weight vector of a next hash function in the sequence. In another aspect, the determined weight vector maximizes an accuracy measure and a variance measure. In still another aspect, a method includes generating an adjusted covariance matrix and generating a sequence of hash functions from the adjusted covariance matrix. In still another aspect, a method includes sequentially generating a sequence of hash functions, where the weight vectors for any previously generated hash functions are used to identify constraints used to generate the weight vector for each next hash function in the sequence.
    • 方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于生成散列函数。 一方面,一种方法包括:通过依次确定每个散列函数的加权向量来产生哈希函数,以使从当前约束权重导出的精度度量最大化并更新当前约束权重,以用于计算下一个哈希函数的权重向量 序列。 在另一方面,确定的权重向量使精度测量和方差测量最大化。 在另一方面,一种方法包括生成经调整的协方差矩阵,并从调整的协方差矩阵生成散列函数序列。 在另一方面,一种方法包括依次生成散列函数序列,其中用于任何先前生成的散列函数的加权向量用于识别用于生成序列中每个下一个散列函数的加权向量的约束。