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    OBJECT DETECTION APPARATUS, OBJECT DETECTION METHOD, AND COMPUTER READABLE RECORDING MEDIUM
    12.
    发明申请
    OBJECT DETECTION APPARATUS, OBJECT DETECTION METHOD, AND COMPUTER READABLE RECORDING MEDIUM 有权

    公开(公告)号：US20210326608A1

    公开(公告)日：2021-10-21

    申请号：US16625136

    申请日：2017-06-23

    Applicant: NEC Corporation

    Inventor： Kosuke YOSHIMI

    IPC: G06K9/00 ,  G06T7/70 ,  G06T7/20 ,  G06T5/00

    Abstract: An object detection apparatus 100 is an apparatus for detecting an object in a fish-eye image. The object detection apparatus 100 includes a normalized image acquisition unit 10 configured to acquire a normalized image obtained by normalizing a fish-eye image in which an object appears; a position detecting unit 20 configured to detect position coordinates of the object in the normalized image; and a determination unit 30 configured to determine a positional relationship between the object and the object detection apparatus 100 using the position coordinates of the object in the normalized image.

    LABELLING TRAINING METHOD AND SYSTEM FOR IMPLEMENTING THE SAME
    13.
    发明申请
    LABELLING TRAINING METHOD AND SYSTEM FOR IMPLEMENTING THE SAME 有权

    公开(公告)号：US20210287058A1

    公开(公告)日：2021-09-16

    申请号：US16817617

    申请日：2020-03-13

    Applicant: NEC Corporation

    Inventor： Kosuke YOSHIMI

    IPC: G06K9/66 ,  G06K9/68 ,  G06N20/00

    Abstract: A method of training a model includes generating processed data by analyzing an input related to an image using a pre-trained model to detect an object in the input. The method includes generating moving area detection data by analyzing the input to determine whether any areas within the input exhibit movement. The method includes generating temporal inconsistency data by analyzing the processed data to determine whether any areas within the processed data exhibit a temporal inconsistency. The method includes identifying whether the input contains an error candidate based on the moving area detection data and the temporal inconsistency data. The method includes receiving confirmation information regarding whether the error candidate is an undetected object within the input. The method includes updating the pre-trained model in response to receiving the confirmation information indicating that the error candidate is the undetected object within the input.

    IMAGING SYSTEM, IMAGING METHOD, AND NON-TRANSITORY RECORDING MEDIUM
    18.
    发明申请
    IMAGING SYSTEM, IMAGING METHOD, AND NON-TRANSITORY RECORDING MEDIUM 有权

    公开(公告)号：US20250104473A1

    公开(公告)日：2025-03-27

    申请号：US18727376

    申请日：2022-01-11

    Applicant: NEC Corporation

    Inventor： Kosuke YOSHIMI

    IPC: G06V40/19 ,  H04N23/45 ,  H04N23/55

    Abstract: An imaging system includes: a first camera with a first focal length; a second camera with a second focal length; a first mirror disposed to correspond to both the first camera and the second camera; and a first adjustment unit that adjusts an optical positional relation between the first camera or the second camera and the first mirror, in accordance with which of the first camera and the second camera is used to image a target. According to such an imaging system, it is possible to properly image targets located at differing distances.

    MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND RECORDING MEDIUM STORING MACHINE LEARNING PROGRAM
    19.
    发明申请
    MACHINE LEARNING DEVICE, MACHINE LEARNING METHOD, AND RECORDING MEDIUM STORING MACHINE LEARNING PROGRAM 有权

    公开(公告)号：US20250069205A1

    公开(公告)日：2025-02-27

    申请号：US18945732

    申请日：2024-11-13

    Applicant: NEC Corporation

    Inventor： Kosuke YOSHIMI

    IPC: G06T5/77 ,  G06T3/60

    Abstract: This machine-learning device is provided with: a detection unit which detects a loss of consistency with a lapse of time in a determination result for unit data, the determination result being output from a determination unit that generates a learning model to be used when performing prescribed determination for one or more pieces of the unit data that form time series data; and a selection unit which selects, on the basis of the result of detection by the detection unit, unit data to be used as teacher data when the determination unit updates the learning model, thereby efficiently raising the accuracy of the learning model when machine learning is performed on the basis of the time series data.

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