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    SEGMENTATION CO-CLUSTERING
    1.
    发明申请
    SEGMENTATION CO-CLUSTERING 有权
    分段协同

    公开(公告)号：US20140079316A1

    公开(公告)日：2014-03-20

    申请号：US14028833

    申请日：2013-09-17

    Applicant: Raytheon BBN Technologies Corp.

    Inventor： Shiv N. Vitaladevuni ,  Rohit Prasad ,  Premkumar Natarajan

    IPC: G06T7/00

    CPC classification number: G06T7/0079 ,  G06K9/34 ,  G06K9/469 ,  G06K2209/01 ,  G06T7/11 ,  G06T7/162 ,  G06T2207/20081 ,  G06T2207/30176

    Abstract: An approach to segmentation or clustering of a set of elements combines separate procedures and uses training data for those procedures on labeled data. This approach is applied to elements being components of an image of text (e.g., printed or handwritten). In some examples, the elements are connected sets of pixels. In images of text, the clusters can correspond to individual lines. The approach provides improved clustering performance as compared to any one of the procedures taken alone.

    Abstract translation: 一组元素的分割或聚类方法组合了单独的过程，并使用标记数据上的那些程序的训练数据。 该方法适用于作为文本图像（例如，打印或手写）的组件的元素。 在一些示例中，元素是连接的像素集合。 在文本的图像中，群集可以对应于单独的行。 与单独采用的任何一个步骤相比，该方法提供了改进的聚类性能。

    FAST COMPUTATION OF KERNEL DESCRIPTORS
    2.
    发明申请
    FAST COMPUTATION OF KERNEL DESCRIPTORS 有权
    KERNEL DESCRIPTOR的快速计算

    公开(公告)号：US20140099033A1

    公开(公告)日：2014-04-10

    申请号：US14046194

    申请日：2013-10-04

    Applicant: Raytheon BBN Technologies Corp.

    Inventor： Pradeep Natarajan ,  Shuang Wu ,  Rohit Prasad ,  Premkumar Natarajan

    IPC: G06K9/46

    CPC classification number: G06K9/4633 ,  G06K9/6247

    Abstract: An approach to computation of kernel descriptors is accelerated using precomputed tables. In one aspect, a fast algorithm for kernel descriptor computation that takes O(1) operations per pixel in each patch, based on pre-computed kernel values. This speeds up the kernel descriptor features under consideration, to levels that are comparable with D-SIFT and color SIFT, and two orders of magnitude faster than STIP and HoG3D. In some examples, kernel descriptors are applied to extract gradient, flow and texture based features for video analysis. In tests of the approach on a large database of internet videos used in the TRECVID MED 2011 evaluations, the flow based kernel descriptors are up to two orders of magnitude faster than STIP and HoG3D, and also produce significant performance improvements. Further, using features from multiple color planes produces small but consistent gains.

    Abstract translation: 使用预先计算的表加速了内核描述符的计算方法。 在一个方面，一种用于内核描述符计算的快速算法，其基于预先计算的内核值在每个补丁中每像素执行O（1）个操作。 这将加速考虑的内核描述符功能，达到与D-SIFT和颜色SIFT相当的水平，比STIP和HoG3D快两个数量级。 在一些示例中，内核描述符被应用于提取用于视频分析的梯度，流和纹理的特征。 在对TRECVID MED 2011评估中使用的大量互联网视频数据库的方法进行测试时，基于流的内核描述符比STIP和HoG3D快两个数量级，并且还可以显着提高性能。 此外，使用来自多个颜色平面的特征产生小但恒定的增益。

    Fast computation of kernel descriptors
    6.
    发明授权
    Fast computation of kernel descriptors 有权
    快速计算内核描述符

    公开(公告)号：US09330332B2

    公开(公告)日：2016-05-03

    申请号：US14046194

    申请日：2013-10-04

    Applicant: Raytheon BBN Technologies Corp.

    Inventor： Pradeep Natarajan ,  Shuang Wu ,  Rohit Prasad ,  Premkumar Natarajan

    IPC: G06K9/56 ,  G06K9/46 ,  G06K9/62

    CPC classification number: G06K9/4633 ,  G06K9/6247

    Abstract: An approach to computation of kernel descriptors is accelerated using precomputed tables. In one aspect, a fast algorithm for kernel descriptor computation that takes O(1) operations per pixel in each patch, based on pre-computed kernel values. This speeds up the kernel descriptor features under consideration, to levels that are comparable with D-SIFT and color SIFT, and two orders of magnitude faster than STIP and HoG3D. In some examples, kernel descriptors are applied to extract gradient, flow and texture based features for video analysis. In tests of the approach on a large database of internet videos used in the TRECVID MED 2011 evaluations, the flow based kernel descriptors are up to two orders of magnitude faster than STIP and HoG3D, and also produce significant performance improvements. Further, using features from multiple color planes produces small but consistent gains.

    Abstract translation: 使用预先计算的表加速了内核描述符的计算方法。 在一个方面，一种用于内核描述符计算的快速算法，其基于预先计算的内核值在每个补丁中每像素执行O（1）个操作。 这将加速考虑的内核描述符功能，达到与D-SIFT和颜色SIFT相当的水平，比STIP和HoG3D快两个数量级。 在一些示例中，内核描述符被应用于提取用于视频分析的梯度，流和纹理的特征。 在对TRECVID MED 2011评估中使用的大量互联网视频数据库的方法进行测试时，基于流的内核描述符比STIP和HoG3D快两个数量级，并且还可以显着提高性能。 此外，使用来自多个颜色平面的特征产生小但恒定的增益。

    Segmentation co-clustering
    7.
    发明授权
    Segmentation co-clustering 有权
    分割共聚集

    公开(公告)号：US09224207B2

    公开(公告)日：2015-12-29

    申请号：US14028833

    申请日：2013-09-17

    Applicant: Raytheon BBN Technologies Corp.

    Inventor： Shiv N. Vitaladevuni ,  Rohit Prasad ,  Premkumar Natarajan

    IPC: G06T7/00 ,  G06K9/34 ,  G06K9/46

    CPC classification number: G06T7/0079 ,  G06K9/34 ,  G06K9/469 ,  G06K2209/01 ,  G06T7/11 ,  G06T7/162 ,  G06T2207/20081 ,  G06T2207/30176

    Abstract: An approach to segmentation or clustering of a set of elements combines separate procedures and uses training data for those procedures on labeled data. This approach is applied to elements being components of an image of text (e.g., printed or handwritten). In some examples, the elements are connected sets of pixels. In images of text, the clusters can correspond to individual lines. The approach provides improved clustering performance as compared to any one of the procedures taken alone.

    Abstract translation: 一组元素的分割或聚类方法组合了单独的过程，并使用标记数据上的那些程序的训练数据。 该方法适用于作为文本图像（例如，打印或手写）的组件的元素。 在一些示例中，元素是连接的像素集合。 在文本的图像中，群集可以对应于单独的行。 与单独采用的任何一个步骤相比，该方法提供了改进的聚类性能。

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