Image processing method, image processing apparatus, and recording medium

    公开(公告)号:US10210420B2

    公开(公告)日:2019-02-19

    申请号:US15450360

    申请日:2017-03-06

    Abstract: An image processing method includes acquiring consecutive time-series images captured by an onboard camera and including at least one image having a first annotation indicating a first region; determining, for each of the images, in reverse chronological order from an image of the last time point, whether the first region exists in the image based on whether the first annotation is attached; identifying the first image of a first time point for which the first region is determined not to exist, and setting a second region including a partial region of an object in the identified first image, indicating the moving object that is obstructed by the object before appearing on the path, and having dimensions based on dimensions of the first region in an image of a second time point immediately after the first time point; and attaching a second annotation to the image corresponding to the second time point, the second annotation indicating the second region.

    TRAINING METHOD, TRAINING DEVICE, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM

    公开(公告)号:US20240086774A1

    公开(公告)日:2024-03-14

    申请号:US18504300

    申请日:2023-11-08

    CPC classification number: G06N20/00

    Abstract: A training method performed through batch learning by a computer includes: obtaining training data including first time-series data and second time-series data different from the first time-series data; performing first training processing of training a neural process (NP) model, which outputs, using a stochastic process, a prediction result that takes uncertainty into account, to predict first and second time-series data distributions, based on the first time-series data and second time-series data; and performing, using a contrastive learning algorithm, second training processing of (i) training the NP model to bring close to each other first sampling data items generated by sampling from the first time-series data distribution, (ii) training the NP model to bring close to each other second sampling data items generated by sampling from the second time-series data distribution, and (iii) training the NP model to push away the first and second sampling data items far from each other.

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