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    DATA SELECTION METHOD, DATA SELECTION APPARATUS AND PROGRAM
    1.
    发明申请
    DATA SELECTION METHOD, DATA SELECTION APPARATUS AND PROGRAM 有权

    公开(公告)号：US20220335085A1

    公开(公告)日：2022-10-20

    申请号：US17631396

    申请日：2019-07-30

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Shunsuke TSUKATANI ,  Kazuhiko MURASAKI ,  Shingo ANDO ,  Atsushi SAGATA

    IPC: G06F16/906

    Abstract: A data selection method selects, based on a set of labeled first data pieces and a set of unlabeled second data pieces, a target to be labeled from the set of the second data pieces. The method includes: a classification procedure classifying data pieces belonging to the set of the first data pieces and data pieces belonging to the set of the second data pieces into clusters of the number at least one more than the number of types of the labels; and a selection procedure selecting the second data piece to be labeled from a cluster, from among the clusters, that does not include the first data piece, each of the procedures being performed by a computer. Thereby, it is possible to select the data piece to be labeled, which is effective for a target task, from among data sets of unlabeled data pieces.

    POINT CLOUD ANALYSIS DEVICE, ESTIMATION DEVICE, POINT CLOUD ANALYSIS METHOD, AND PROGRAM
    2.
    发明申请
    POINT CLOUD ANALYSIS DEVICE, ESTIMATION DEVICE, POINT CLOUD ANALYSIS METHOD, AND PROGRAM 有权

    公开(公告)号：US20220230347A1

    公开(公告)日：2022-07-21

    申请号：US17608975

    申请日：2019-05-08

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Hitoshi NIIGAKI ,  Masaki WAKI ,  Masaaki INOUE ,  Yasuhiro YAO ,  Tomoya SHIMIZU ,  Hiroyuki OSHIDA ,  Kana KURATA ,  Shingo ANDO ,  Atsushi SAGATA

    IPC: G06T7/66 ,  G06V10/25 ,  G06V10/75

    Abstract: It is possible to estimate a slack level accurately in consideration of a shape of a deformed cable. A point cloud analysis device sets a plurality of regions of interest obtained by window-searching a wire model including a quadratic curve model representing a cable obtained from a point cloud consisting of three-dimensional points on an object, the region of interest being divided into a first region and a second region. The point cloud analysis device compares information on the first region with information on the second region based on the point cloud included in the region of interest and the quadratic curve model for each of the plurality of regions of interest, calculates a degree of division boundary representing a degree to which a division position between the first region and the second region of the plurality of regions of interest is a branch point of the cable, and detects a division boundary point that is a branch point of a cable represented by the quadratic curve model based on the degree of division boundary calculated for each of the plurality of regions of interest.

    IMAGE PROCESSING DEVICE, METHOD, AND PROGRAM
    3.
    发明申请
    IMAGE PROCESSING DEVICE, METHOD, AND PROGRAM 有权

    公开(公告)号：US20220058807A1

    公开(公告)日：2022-02-24

    申请号：US17413429

    申请日：2019-12-09

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Shunsuke TSUKATANI ,  Kazuhiko MURASAKI ,  Shingo ANDO ,  Atsushi SAGATA

    IPC: G06T7/174 ,  G06K9/62 ,  G06T5/50

    Abstract: Labels can be accurately identified even for an image with a resolution not used in training data. Based on an input image, a resolution of the input image, and a resolution of a training image used for training a trained model of assigning labels to pixels of an image, a plurality of low-resolution images are generated from the input image by using a plurality of shift amounts for a pixel correspondence between the input image and the respective low-resolution images with a resolution corresponding to the training image, the low-resolution images are input to the trained model, a plurality of low-resolution label images is output in which pixels of the respective low-resolution images are assigned labels, and a label image is output in which labels for pixels of the input image are obtained, based on the shift amounts used for generating the low-resolution images and the low-resolution label images.

    DETECTION LEARNING DEVICE, METHOD, AND PROGRAM
    4.
    发明申请
    DETECTION LEARNING DEVICE, METHOD, AND PROGRAM 有权

    公开(公告)号：US20220019899A1

    公开(公告)日：2022-01-20

    申请号：US17312364

    申请日：2019-12-02

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Kazuhiko MURASAKI ,  Chihiro SAITO ,  Shingo ANDO ,  Atsushi SAGATA

    IPC: G06N3/08

    Abstract: A weft-balanced detector can be trained in the vicinity of a desired TPR or PPR. A range determined by an upper limit and a lower limit of a. true positive rate or a false positive rate for defining a part of an area under a ROC curve is set so as to be narrowed at each repetition, a score function is trained so as to optimize an objective function represented using positive example data selected from ranked positive example data, negative example data, and the score function that calculates a score representing likelihood of a positive example according to the set range between the upper limit and the lower limit of the true positive rate or the false positive rate, the positive example data is ranked, the maximization learning unit and the ranking unit repeats the processing until the objective function is converged, and the region-to-be-maximized setting unit repeats setting until the range between the upper limit and the lower limit of the true positive rate or the false positive rate becomes a predetermined size.

    IMAGE CLASSIFIER LEARNING DEVICE, IMAGE CLASSIFIER LEARNING METHOD, AND PROGRAM
    6.
    发明申请
    IMAGE CLASSIFIER LEARNING DEVICE, IMAGE CLASSIFIER LEARNING METHOD, AND PROGRAM 有权

    公开(公告)号：US20210357698A1

    公开(公告)日：2021-11-18

    申请号：US17277248

    申请日：2019-09-06

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Kazuhiko MURASAKI ,  Shingo ANDO ,  Atsushi SAGATA

    IPC: G06K9/62

    Abstract: An object is to make it possible to train an image recognizer by efficiently using training data that does not include label information. A determination unit 180 causes repeated execution of the followings. A feature representation model for extracting feature vectors of pixels is trained such that an objective function is minimized, the objective function being expressed as a function that includes a value that is based on a difference between a distance between feature vectors of pixels labeled with a positive example label and a distance between a feature vector of a pixel labeled with the positive example label and a feature vector of an unlabeled pixel, and a value that is based on a difference between a distance between a feature vector of a pixel labeled with the positive example label and a feature vector of an unlabeled pixel and a distance between a feature vector of a pixel labeled with the positive example label and a feature vector of a pixel labeled with a negative example label, and based on a distribution of feature vectors corresponding to the positive example label, a predetermined number of labels are given based on the likelihood that each unlabeled pixel is a positive example.

    OBJECT DETECTION AND RECOGNITION DEVICE, METHOD, AND PROGRAM
    9.
    发明申请
    OBJECT DETECTION AND RECOGNITION DEVICE, METHOD, AND PROGRAM 有权

    公开(公告)号：US20220101628A1

    公开(公告)日：2022-03-31

    申请号：US17422092

    申请日：2019-12-26

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Yongqing SUN ,  Jun SHIMAMURA ,  Atsushi SAGATA

    IPC: G06V20/00 ,  G06V10/25 ,  G06V10/82 ,  G06T3/40

    Abstract: The category and region of an object shown by an image can be accurately recognized.
    A first hierarchical feature map generation unit 23 Generates a hierarchical feature map constituted of feature maps hierarchized from a deep layer to a shallow layer, based on feature maps which are output by layers of the CNN. A second hierarchical feature map generation unit 24 generates a hierarchical feature map constituted of feature maps hierarchized from the shallow layer to the deep layer. As integration unit 25 generates a hierarchical feature map by integrating feature maps of corresponding layers. An object region detection unit 26 detects object candidate regions and an object recognition unit 27 recognizes, for each of the object candidate regions, the category and region of as object represented by the object candidate region.

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