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    OBJECT DEFECT CORRECTION
    1.
    发明申请
    OBJECT DEFECT CORRECTION 有权

    公开(公告)号：US20210304389A1

    公开(公告)日：2021-09-30

    申请号：US16835785

    申请日：2020-03-31

    Applicant: International Business Machines Corporation

    Inventor： Jinfeng Li ,  Guo Qiang Hu ,  Jian Xu ,  Fan Li ,  JingChang Huang ,  Jun Zhu

    IPC: G06T7/00 ,  G06N20/00 ,  G06K9/62

    Abstract: According to embodiments of the present invention, a method, a device and a computer program product for image processing is provided. A computing device obtains an image of a first object, the image presenting a defect of the first object. A computing device obtains defect distribution information indicating respective frequencies of a plurality of predetermined categories of defects presented at corresponding locations in a plurality of training images, the plurality of training images presenting second objects and being used for training a defect classifier. A computing device determines a target category of the defect of the first object by applying the image and the defect distribution information to the defect classifier. A computing device generates one or more correction notifications.

    IMAGE RECOVERY
    3.
    发明申请
    IMAGE RECOVERY 有权

    公开(公告)号：US20210118113A1

    公开(公告)日：2021-04-22

    申请号：US16654962

    申请日：2019-10-16

    Applicant: International Business Machines Corporation

    Inventor： Fan Li ,  Guo Qiang Hu ,  Jun Zhu ,  Sheng Nan Zhu ,  JingChang Huang ,  Yuan Yuan Ding ,  Peng Ji

    IPC: G06T7/00 ,  G06K9/62 ,  G06T11/60 ,  G06F17/18

    Abstract: A method, a device and a computer program product for image processing are proposed. In the method, a first training image and region information are obtained. The region information indicates a region of a defect in the first training image. A second training image with the defect at least partially removed is generated using an image generator based on the first training image and the region information. The image generator is trained to recover the first training image by replacing pixels included in the region indicated by the region information. The image generator is updated based on the second training image. In this way, the image including the defect can be accurately and efficiently recovered.

    Image recovery
    5.
    发明授权
    Image recovery 有权

    公开(公告)号：US11295439B2

    公开(公告)日：2022-04-05

    申请号：US16654962

    申请日：2019-10-16

    Applicant: International Business Machines Corporation

    Inventor： Fan Li ,  Guo Qiang Hu ,  Jun Zhu ,  Sheng Nan Zhu ,  JingChang Huang ,  Yuan Yuan Ding ,  Peng Ji

    IPC: G06T11/60 ,  G06T7/00 ,  G06K9/62 ,  G06F17/18

    Abstract: A method, a device and a computer program product for image processing are proposed. In the method, a first training image and region information are obtained. The region information indicates a region of a defect in the first training image. A second training image with the defect at least partially removed is generated using an image generator based on the first training image and the region information. The image generator is trained to recover the first training image by replacing pixels included in the region indicated by the region information. The image generator is updated based on the second training image. In this way, the image including the defect can be accurately and efficiently recovered.

    FEATURE DETECTION BASED ON NEURAL NETWORKS
    6.
    发明申请
    FEATURE DETECTION BASED ON NEURAL NETWORKS 有权

    公开(公告)号：US20220092756A1

    公开(公告)日：2022-03-24

    申请号：US17026620

    申请日：2020-09-21

    Applicant: International Business Machines Corporation

    Inventor： JingChang Huang ,  GUO QIANG HU ,  Peng Ji ,  Yuan Yuan Ding ,  Sheng Nan Zhu ,  Jinfeng Li

    IPC: G06T7/00 ,  G06N3/08 ,  G06K9/00 ,  G06N3/04 ,  G06T3/40

    Abstract: A plurality of different images of a same region of interest in an object are input into a set of neural networks, wherein each image of the region has been captured under a different value of a variable condition. A classification for each image is generated by the set of neural networks, wherein each classification includes a confidence score in a prediction of whether a feature is present in the region. The image classifications are ensembled to generate a final classification for the region. By applying a loss function, a loss is computed based on comparing the final classification to a ground truth of whether the feature is present in the region. The parameters of the set of neural networks are adjusted based on the computed loss.

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