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    Generating class-agnostic object masks in digital images
    32.
    发明授权
    Generating class-agnostic object masks in digital images 有权

    公开(公告)号：US11587234B2

    公开(公告)日：2023-02-21

    申请号：US17151111

    申请日：2021-01-15

    Applicant: Adobe Inc.

    Inventor： Yinan Zhao ,  Brian Price ,  Scott Cohen

    IPC: G06T7/11 ,  G06T7/168 ,  G06T9/00 ,  G06T5/00 ,  G06T7/181

    Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

    Image object segmentation based on temporal information
    34.
    发明授权
    Image object segmentation based on temporal information 有权

    公开(公告)号：US11379987B2

    公开(公告)日：2022-07-05

    申请号：US17020023

    申请日：2020-09-14

    Applicant: ADOBE INC.

    Inventor： Ning Xu ,  Brian Price ,  Scott Cohen

    IPC: G06T7/11 ,  G06T7/194 ,  G06T7/73 ,  G11B27/031

    Abstract: A temporal object segmentation system determines a location of an object depicted in a video. In some cases, the temporal object segmentation system determines the object's location in a particular frame of the video based on information indicating a previous location of the object in a previous video frame. For example, an encoder neural network in the temporal object segmentation system extracts features describing image attributes of a video frame. A convolutional long-short term memory neural network determines the location of the object in the frame, based on the extracted image attributes and information indicating a previous location in a previous frame. A decoder neural network generates an image mask indicating the object's location in the frame. In some cases, a video editing system receives multiple generated masks for a video, and modifies one or more video frames based on the locations indicated by the masks.

    Image white balancing
    35.
    发明授权
    Image white balancing 有权

    公开(公告)号：US11044450B2

    公开(公告)日：2021-06-22

    申请号：US16435371

    申请日：2019-06-07

    Applicant: Adobe Inc. ,  York University

    Inventor： Mahmoud Afifi ,  Michael Brown ,  Brian Price ,  Scott Cohen

    IPC: H04N9/73 ,  G06K9/62 ,  G06T5/40

    Abstract: Techniques are described for white balancing an input image by determining a color transformation for the input image based on color transformations that have been computed for training images whose color characteristics are similar to those of the input image. Techniques are also described for generating a training dataset comprising color information for a plurality of training images and color transformation information for the plurality of training images. The color information in the training dataset is searched to identify a subset of training images that are most similar in color to the input image. The color transformation for the input image is then computed by combining color transformation information for the identified training images. The contribution of the color transformation information for any given training image to the combination can be weighted based on the degree of color similarity between the input image and the training image.

    Interactive image matting using neural networks
    36.
    发明授权
    Interactive image matting using neural networks 有权

    公开(公告)号：US11004208B2

    公开(公告)日：2021-05-11

    申请号：US16365213

    申请日：2019-03-26

    Applicant: Adobe Inc.

    Inventor： Brian Lynn Price ,  Scott Cohen ,  Marco Forte ,  Ning Xu

    IPC: G06T7/194 ,  G06T7/136 ,  G06T7/90 ,  G06T3/40 ,  G06N3/04 ,  G06N3/08 ,  G06K9/46

    Abstract: Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.

    MULTI-MODULE AND MULTI-TASK MACHINE LEARNING SYSTEM BASED ON AN ENSEMBLE OF DATASETS
    38.
    发明申请
    MULTI-MODULE AND MULTI-TASK MACHINE LEARNING SYSTEM BASED ON AN ENSEMBLE OF DATASETS 审中-公开

    公开(公告)号：US20200349464A1

    公开(公告)日：2020-11-05

    申请号：US16401548

    申请日：2019-05-02

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Trung Huu Bui ,  Scott Cohen ,  Mingyang Ling ,  Chenyun Wu

    IPC: G06N20/00

    Abstract: Techniques and systems are provided for training a machine learning model using different datasets to perform one or more tasks. The machine learning model can include a first sub-module configured to perform a first task and a second sub-module configured to perform a second task. The first sub-module can be selected for training using a first training dataset based on a format of the first training dataset. The first sub-module can then be trained using the first training dataset to perform the first task. The second sub-module can be selected for training using a second training dataset based on a format of the second training dataset. The second sub-module can then be trained using the second training dataset to perform the second task.

    INTERACTIVE IMAGE MATTING USING NEURAL NETWORKS
    39.
    发明申请
    INTERACTIVE IMAGE MATTING USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200311946A1

    公开(公告)日：2020-10-01

    申请号：US16365213

    申请日：2019-03-26

    Applicant: Adobe Inc.

    Inventor： Brian Lynn Price ,  Scott Cohen ,  Marco Forte ,  Ning Xu

    IPC: G06T7/194 ,  G06T7/136 ,  G06T3/40 ,  G06T7/90

    Abstract: Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.

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