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    METHOD AND SYSTEM FOR CELL IMAGE SEGMENTATION USING MULTI-STAGE CONVOLUTIONAL NEURAL NETWORKS
    1.
    发明申请
    METHOD AND SYSTEM FOR CELL IMAGE SEGMENTATION USING MULTI-STAGE CONVOLUTIONAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190228268A1

    公开(公告)日：2019-07-25

    申请号：US16315560

    申请日：2017-08-09

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Yongmian ZHANG ,  Jingwen ZHU

    IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

    Abstract: An artificial neural network system for image classification, including multiple independent individual convolutional neural networks (CNNs) connected in multiple stages, each CNN configured to process an input image to calculate a pixelwise classification. The output of an earlier stage CNN, which is a class score image having identical height and width as its input image and a depth of N representing the probabilities of each pixel of the input image belonging to each of N classes, is input into the next stage CNN as input image. When training the network system, the first stage CNN is trained using first training images and corresponding label data; then second training images are forward propagated by the trained first stage CNN to generate corresponding class score images, which are used along with label data corresponding to the second training images to train the second stage CNN.

    METHOD AND APPARATUS FOR INTERACTIVE USER INTERFACE WITH WEARABLE DEVICE
    2.
    发明申请
    METHOD AND APPARATUS FOR INTERACTIVE USER INTERFACE WITH WEARABLE DEVICE 有权
    用于具有可接受设备的交互式用户界面的方法和装置

    公开(公告)号：US20160252976A1

    公开(公告)日：2016-09-01

    申请号：US14632661

    申请日：2015-02-26

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Yongmian ZHANG ,  Jingwen ZHU ,  Toshiki OHINATA ,  Haisong GU

    IPC: G06F3/03 ,  G06K9/00 ,  H04N5/33 ,  G06F3/00 ,  G06F3/01

    CPC classification number: G06F3/0317 ,  G06F3/011 ,  G06F3/038 ,  G06K9/00375

    Abstract: A method and system are disclosed for recognizing an object, the method including emitting one or more arranged patterns of infrared rays (IR) from an infrared emitter towards a projection region, the one or more arranged patterns of infrared rays forming unique dot patterns; mapping the one or more arranged patterns of infrared rays on the operation region to generate a reference image; capturing an IR image and a RGB image of an object with a wearable device, the wearable device including an infrared (IR) camera and a RGB camera; extracting IR dots from the IR image and determining a match between the extracted IR dots and the reference image; determining a position of the RGB image on the reference image; and mapping the position of the RGB image to a coordinate on the projection region.

    Abstract translation: 公开了一种用于识别物体的方法和系统，该方法包括从红外发射器朝向投影区域发射一个或多个布置的红外线（IR）图案，所述一个或多个排列的红外线图案形成唯一的点图案; 将所述一个或多个布置的红外线图案映射在所述操作区域上以生成参考图像; 使用可穿戴装置拍摄对象的IR图像和RGB图像，所述可佩戴装置包括红外（IR）相机和RGB相机; 从IR图像提取IR点并确定提取的IR点和参考图像之间的匹配; 确定参考图像上的RGB图像的位置; 并将RGB图像的位置映射到投影区域上的坐标。

    METHOD AND SYSTEM FOR MULTI-SCALE CELL IMAGE SEGMENTATION USING MULTIPLE PARALLEL CONVOLUTIONAL NEURAL NETWORKS
    5.
    发明申请
    METHOD AND SYSTEM FOR MULTI-SCALE CELL IMAGE SEGMENTATION USING MULTIPLE PARALLEL CONVOLUTIONAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190236411A1

    公开(公告)日：2019-08-01

    申请号：US16315541

    申请日：2017-08-09

    Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

    Inventor： Jingwen ZHU ,  Yongmian ZHANG

    IPC: G06K9/62 ,  G06N3/04 ,  G06T3/40

    CPC classification number: G06K9/6256 ,  G06K9/4628 ,  G06K9/6268 ,  G06K9/6274 ,  G06K9/6292 ,  G06N3/0454 ,  G06T3/4046

    Abstract: An artificial neural network system for image classification, formed of multiple independent individual convolutional neural networks (CNNs), each CNN being configured to process an input image patch to calculate a classification for the center pixel of the patch. The multiple CNNs have different receptive field of views for processing image patches of different sizes centered at the same pixel. A final classification for the center pixel is calculated by combining the classification results from the multiple CNNs. An image patch generator is provided to generate the multiple input image patches of different sizes by cropping them from the original input image. The multiple CNNs have similar configurations, and when training the artificial neural network system, one CNN is trained first, and the learned parameters are transferred to another CNN as initial parameters and the other CNN is further trained. The classification includes three classes, namely background, foreground, and edge.

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