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公开(公告)号：US20150213583A1
公开(公告)日：2015-07-30
申请号：US14163910
申请日：2014-01-24
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Mohammad Rastegari , Aaron P. Hertzmann , Elya Shechtman
CPC classification number: G06T5/005 , G06T3/4015 , G06T3/4053 , G06T5/001 , G06T5/002 , G06T5/003 , G06T5/50 , G06T2207/20081 , G06T2207/20221
Abstract: An image prior as a shared basis mixture model is described. In one or more implementations, a plurality of image patches are generated from one or more images. A shared basis mixture model is learned to model an image patch distribution of the plurality of image patches from the one or more images as part of a Gaussian mixture model. An image may then be reconstructed using the shared basis mixture model as an image prior.
Abstract translation: 描述作为共享基础混合模型的图像。 在一个或多个实现中，从一个或多个图像生成多个图像块。 学习共享基础混合模型，以将来自一个或多个图像的多个图像块的图像补丁分布建模为高斯混合模型的一部分。 然后可以使用共享基础混合模型来重构图像作为图像。
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公开(公告)号：US10032092B2
公开(公告)日：2018-07-24
申请号：US15013641
申请日：2016-02-02
Applicant: Adobe Systems Incorporated
Inventor： Aaron P. Hertzmann , Saining Xie , Bryan C. Russell
Abstract: Techniques are described to generate improved training data for pixel labeling. To generate training data, objects are displayed in a user interface by a computing device, e.g., iteratively. The objects are taken from a structured object representation associated with a respective one of a plurality of images. The structured object representation defines a hierarchical relationship of the objects within the respective image. Inputs are then received that are originated through user interaction with the user interface. The inputs label respective ones of the iteratively displayed objects, e.g., as text, a graphical element, background, foreground, and so forth. A model is trained by the computing device using machine learning.
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公开(公告)号：US20170220903A1
公开(公告)日：2017-08-03
申请号：US15013641
申请日：2016-02-02
Applicant: Adobe Systems Incorporated
Inventor： Aaron P. Hertzmann , Saining Xie , Bryan C. Russell
CPC classification number: G06K9/6254 , G06K9/00456 , G06K9/469 , G06K9/6256 , G06K9/66
Abstract: Techniques are described to generate improved training data for pixel labeling. To generate training data, objects are displayed in a user interface by a computing device, e.g., iteratively. The objects are taken from a structured object representation associated with a respective one of a plurality of images. The structured object representation defines a hierarchical relationship of the objects within the respective image. Inputs are then received that are originated through user interaction with the user interface. The inputs label respective ones of the iteratively displayed objects, e.g., as text, a graphical element, background, foreground, and so forth. A model is trained by the computing device using machine learning.
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公开(公告)号：US09159123B2
公开(公告)日：2015-10-13
申请号：US14163910
申请日：2014-01-24
Applicant: Adobe Systems Incorporated
Inventor： Mohammad Rastegari , Aaron P. Hertzmann , Elya Shechtman
CPC classification number: G06T5/005 , G06T3/4015 , G06T3/4053 , G06T5/001 , G06T5/002 , G06T5/003 , G06T5/50 , G06T2207/20081 , G06T2207/20221
Abstract: An image prior as a shared basis mixture model is described. In one or more implementations, a plurality of image patches are generated from one or more images. A shared basis mixture model is learned to model an image patch distribution of the plurality of image patches from the one or more images as part of a Gaussian mixture model. An image may then be reconstructed using the shared basis mixture model as an image prior.
Abstract translation: 描述作为共享基础混合模型的图像。 在一个或多个实现中，从一个或多个图像生成多个图像块。 学习共享基础混合模型，以将来自一个或多个图像的多个图像块的图像补丁分布建模为高斯混合模型的一部分。 然后可以使用共享基础混合模型来重构图像作为图像。
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