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    Label estimation device, label estimation method, and label estimation program
    1.
    发明授权
    Label estimation device, label estimation method, and label estimation program 有权

    公开(公告)号：US12136252B2

    公开(公告)日：2024-11-05

    申请号：US17628071

    申请日：2019-07-19

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Yasuhiro Yao ,  Kazuhiko Murasaki ,  Shingo Ando ,  Atsushi Sagata

    IPC: G06K9/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776

    Abstract: A point group including a small number of points that have been assigned labels is taken as an input to assign labels to points that have not been assigned labels.
    In a label estimation apparatus for estimating a label to be assigned to a point that has not been labeled using a label of a point that has been labeled among points included in a point group, a confidence derivation unit 103 takes a point that has not been labeled within a point group including a point that has been labeled and the point that has not been labeled as a target point and estimates a class of the target point and a likelihood indicating a confidence of an estimation result of the class from a set of points included in the point group, a priority derivation unit 104 obtains a distance between the target point and a point that has been assigned the same label as a label corresponding to the estimated class as a priority used to determine whether the estimated class is appropriate, and a label determination unit 105 determines whether the estimated class is appropriate using at least an index based on the distance.

    Depth superresolution device, depth superresolution method, and program
    3.
    发明授权
    Depth superresolution device, depth superresolution method, and program 有权

    公开(公告)号：US11900622B2

    公开(公告)日：2024-02-13

    申请号：US17425916

    申请日：2020-01-27

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Yasuhiro Yao ,  Shingo Ando ,  Kana Kurata ,  Hitoshi Niigaki ,  Atsushi Sagata

    IPC: G06T7/50 ,  G06T3/40

    CPC classification number: G06T7/50 ,  G06T3/4046 ,  G06T2200/04 ,  G06T2207/20084

    Abstract: Dense depth information can be generated using only a monocular image and sparse depth information.
    A depth hyper-resolving apparatus 100 includes: an input data processing unit 22 that outputs a hierarchical input image and hierarchical input depth information by resolution conversion in accordance with a predetermined number of tiers for an input image and input depth information; a depth continuity estimation unit 24 that derives hierarchical estimated depth continuity based on the hierarchical input image; a depth continuity mask deriving unit 26 that outputs a hierarchical depth continuity mask representing values of locations depending on whether a depth is continuous based on the hierarchical input image and the hierarchical estimated depth continuity; and a cost function minimization unit 30 that derives hyper-resolved depth information to minimize a cost function expressed by using the hierarchical input depth information, the hierarchical depth continuity mask, and the hyper-resolved depth information.

    Point cloud annotation device, method, and program
    4.
    发明授权
    Point cloud annotation device, method, and program 有权

    公开(公告)号：US12106438B2

    公开(公告)日：2024-10-01

    申请号：US17608735

    申请日：2019-05-08

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Hitoshi Niigaki ,  Yasuhiro Yao ,  Shingo Ando ,  Kana Kurata ,  Atsushi Sagata

    IPC: G06T19/00 ,  G06T7/00

    CPC classification number: G06T19/00 ,  G06T7/0002 ,  G06T2200/24 ,  G06T2207/10028 ,  G06T2207/20092 ,  G06T2210/56 ,  G06T2219/004

    Abstract: Annotation can be easily performed on a three-dimensional point cloud and a working time can be reduced. An interface unit 22 displays a point cloud indicating a three-dimensional point on an object, and receives designation of a three-dimensional point indicating an annotation target object and designation of a three-dimensional point not indicating the annotation target object. A candidate cluster calculation unit 32 calculates a value of a predetermined evaluation function indicating a likelihood of a point cloud cluster being the annotation target object based on the designation of a three-dimensional point for point cloud clusters obtained by clustering the point clouds. A cluster selection and storage designation unit 34 causes the interface unit 22 to display the point cloud clusters in descending order of the value of the evaluation function, and receives a selection of a point cloud cluster to be annotated. An annotation execution unit 36 executes annotation indicating the annotation target object for each three-dimensional point included in the selected point cloud cluster.

    Image processing device, method, and program
    8.
    发明授权
    Image processing device, method, and program 有权

    公开(公告)号：US12039736B2

    公开(公告)日：2024-07-16

    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: G06N3/0464 ,  G06F18/214 ,  G06T5/50 ,  G06T7/174

    CPC classification number: G06T7/174 ,  G06F18/214 ,  G06T5/50 ,  G06T2207/20081

    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.

    Image classifier learning device, image classifier learning method, and program
    9.
    发明授权
    Image classifier learning device, image classifier learning method, and program 有权

    公开(公告)号：US11989929B2

    公开(公告)日：2024-05-21

    申请号：US17277248

    申请日：2019-09-06

    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION

    Inventor： Kazuhiko Murasaki ,  Shingo Ando ,  Atsushi Sagata

    IPC: G06V10/774 ,  G06F18/214 ,  G06F18/22 ,  G06V10/82

    CPC classification number: G06V10/7753 ,  G06F18/2155 ,  G06F18/22 ,  G06V10/82

    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.

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