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公开(公告)号：US20170358143A1
公开(公告)日：2017-12-14
申请号：US15178237
申请日：2016-06-09
Applicant: Adobe Systems Incorporated
Inventor： Jakub Fiser , Ondrej Jamriska , Michal Lukác , Elya Shechtman , Paul J. Asente , Jingwan Lu , Daniel Sýkora
CPC classification number: G06T19/20 , G06K9/6204 , G06T11/00 , G06T15/02 , G06T15/50 , G06T15/503 , G06T2219/2012 , G06T2219/2024
Abstract: Techniques for controlling patch-usage in image synthesis are described. In implementations, a curve is fitted to a set of sorted matching errors that correspond to potential source-to-target patch assignments between a source image and a target image. Then, an error budget is determined using the curve. In an example, the error budget is usable to identify feasible patch assignments from the potential source-to-target patch assignments. Using the error budget along with uniform patch-usage enforcement, source patches from the source image are assigned to target patches in the target image. Then, at least one of the assigned source patches is assigned to an additional target patch based on the error budget. Subsequently, an image is synthesized based on the source patches assigned to the target patches.
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公开(公告)号：US20170344860A1
公开(公告)日：2017-11-30
申请号：US15679989
申请日：2017-08-17
Applicant: Adobe Systems Incorporated
Inventor： Ian Sachs , Xiaoyong Shen , Sylvain Paris , Aaron Hertzmann , Elya Shechtman , Brian Price
CPC classification number: G06K9/66 , G06K9/00228 , G06K9/4604 , G06T7/11 , G06T7/73 , G06T7/90 , G06T2207/20084 , G06T2207/30201
Abstract: Systems and methods are disclosed for segregating target individuals represented in a probe digital image from background pixels in the probe digital image. In particular, in one or more embodiments, the disclosed systems and methods train a neural network based on two or more of training position channels, training shape input channels, training color channels, or training object data. Moreover, in one or more embodiments, the disclosed systems and methods utilize the trained neural network to select a target individual in a probe digital image. Specifically, in one or more embodiments, the disclosed systems and methods generate position channels, training shape input channels, and color channels corresponding the probe digital image, and utilize the generated channels in conjunction with the trained neural network to select the target individual.
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公开(公告)号：US09773196B2
公开(公告)日：2017-09-26
申请号：US15005855
申请日：2016-01-25
Applicant: Adobe Systems Incorporated
Inventor： Ian Sachs , Xiaoyong Shen , Sylvain Paris , Aaron Hertzmann , Elya Shechtman , Brian Price
CPC classification number: G06K9/66 , G06K9/00228 , G06K9/4604 , G06T7/11 , G06T7/73 , G06T7/90 , G06T2207/20084 , G06T2207/30201
Abstract: Systems and methods are disclosed for segregating target individuals represented in a probe digital image from background pixels in the probe digital image. In particular, in one or more embodiments, the disclosed systems and methods train a neural network based on two or more of training position channels, training shape input channels, training color channels, or training object data. Moreover, in one or more embodiments, the disclosed systems and methods utilize the trained neural network to select a target individual in a probe digital image. Specifically, in one or more embodiments, the disclosed systems and methods generate position channels, training shape input channels, and color channels corresponding the probe digital image, and utilize the generated channels in conjunction with the trained neural network to select the target individual.
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公开(公告)号：US20170249769A1
公开(公告)日：2017-08-31
申请号：US15597911
申请日：2017-05-17
Applicant: Adobe Systems Incorporated
Inventor： Ohad I. Fried , Elya Shechtman , Daniel R. Goldman
CPC classification number: G06T11/60 , G06K9/4671 , G06T5/002 , G06T5/005 , G06T7/11 , G06T2207/10004 , G06T2207/20081
Abstract: Image distractor detection and processing techniques are described. In one or more implementations, a digital medium environment is configured for image distractor detection that includes detecting one or more locations within the image automatically and without user intervention by the one or more computing devices that include one or more distractors that are likely to be considered by a user as distracting from content within the image. The detection includes forming a plurality of segments from the image by the one or more computing devices and calculating a score for each of the plurality of segments that is indicative of a relative likelihood that a respective said segment is considered a distractor within the image. The calculation is performed using a distractor model trained using machine learning as applied to a plurality images having ground truth distractor locations.
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公开(公告)号：US09710898B2
公开(公告)日：2017-07-18
申请号：US14945308
申请日：2015-11-18
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Sohrab Amirghodsi , Elya Shechtman , Daniel Robert Goldman , Aliakbar Darabi
CPC classification number: G06T5/50 , G06T7/11 , G06T7/155 , G06T11/001 , G06T2200/24 , G06T2207/10024
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at image synthesis utilizing an active mask. In one embodiment, input is received that identifies a target region within an image that is to be synthesized. A patch synthesis technique can then be performed to synthesize the target region based on portions of a source region that are identified by the patch synthesis technique. In embodiments, the patch synthesis technique includes, for at least one iteration, generating an active mask that indicates one or more portions of the target region as inactive. This active mask can be utilized by at least one process of the patch synthesis technique to ignore the one or more portions indicated as inactive by the active mask for the at least one iteration of the patch synthesis technique. Other embodiments may be described and/or claimed.
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Patent Agency Ranking
公开(公告)号：US20170032551A1
公开(公告)日：2017-02-02
申请号：US14812841
申请日：2015-07-29
Applicant: Adobe Systems Incorporated
Inventor： Ohad I. Fried , Elya Shechtman , Daniel R. Goldman
CPC classification number: G06T11/60 , G06K9/4671 , G06T5/002 , G06T5/005 , G06T7/11 , G06T2207/10004 , G06T2207/20081
Abstract: Image distractor detection and processing techniques are described. In one or more implementations, a digital medium environment is configured for image distractor detection that includes detecting one or more locations within the image automatically and without user intervention by the one or more computing devices that include one or more distractors that are likely to be considered by a user as distracting from content within the image. The detection includes forming a plurality of segments from the image by the one or more computing devices and calculating a score for each of the plurality of segments that is indicative of a relative likelihood that a respective said segment is considered a distractor within the image. The calculation is performed using a distractor model trained using machine learning as applied to a plurality images having ground truth distractor locations.
Abstract translation: 描述图像牵引器检测和处理技术。 在一个或多个实现中，数字媒体环境被配置用于图像牵引器检测，其包括自动检测图像内的一个或多个位置，并且不需要包括一个或多个可能被考虑的干扰物的一个或多个计算设备的用户干预 由使用者分心图像内的内容。 所述检测包括由所述一个或多个计算装置从所述图像形成多个片段，并且计算所述多个片段中的每一个的分数，所述片段指示相应的所述片段被认为是所述图像内的牵引器的相对似然性。 使用使用机器学习训练的牵引器模型来应用计算，该干扰模型应用于具有地面真实牵引器位置的多个图像。
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公开(公告)号：US09524449B2
公开(公告)日：2016-12-20
申请号：US14107191
申请日：2013-12-16
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Guang Chen , Hailin Jin , Jonathan Brandt , Elya Shechtman , Aseem Omprakash Agarwala
CPC classification number: G06K9/6267 , G06K9/6219 , G06K9/6282 , G06K9/6807
Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.
Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。
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公开(公告)号：US09519954B2
公开(公告)日：2016-12-13
申请号：US14798285
申请日：2015-07-13
Applicant: Adobe Systems Incorporated
Inventor： Elya Shechtman , Jue Wang , Hyunjoon Lee , Seungyong Lee
CPC classification number: H04N5/23229 , G06T3/00 , G06T3/608 , G06T5/00 , G06T5/006 , G06T7/80 , G06T15/20
Abstract: Techniques and apparatus for automatic upright adjustment of digital images. An automatic upright adjustment technique is described that may provide an automated approach for straightening up slanted features in an input image to improve its perceptual quality. This correction may be referred to as upright adjustment. A set of criteria based on human perception may be used in the upright adjustment. A reprojection technique that implements an optimization framework is described that yields an optimal homography for adjustment based on the criteria and adjusts the image according to new camera parameters generated by the optimization. An optimization-based camera calibration technique is described that simultaneously estimates vanishing lines and points as well as camera parameters for an image; the calibration technique may, for example, be used to generate estimates of camera parameters and vanishing points and lines that are input to the reprojection technique.
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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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公开(公告)号：US20150215531A1
公开(公告)日：2015-07-30
申请号：US14681913
申请日：2015-04-08
Applicant: Adobe Systems Incorporated
Inventor： Elya Shechtman , Jue Wang , Hyunjoon Lee , Seungyong Lee
CPC classification number: H04N5/23229 , G06T3/00 , G06T3/608 , G06T5/00 , G06T5/006 , G06T7/80 , G06T15/20
Abstract: Techniques and apparatus for automatic upright adjustment of digital images. An automatic upright adjustment technique is described that may provide an automated approach for straightening up slanted features in an input image to improve its perceptual quality. This correction may be referred to as upright adjustment. A set of criteria based on human perception may be used in the upright adjustment. A reprojection technique that implements an optimization framework is described that yields an optimal homography for adjustment based on the criteria and adjusts the image according to new camera parameters generated by the optimization. An optimization-based camera calibration technique is described that simultaneously estimates vanishing lines and points as well as camera parameters for an image; the calibration technique may, for example, be used to generate estimates of camera parameters and vanishing points and lines that are input to the reprojection technique.
Abstract translation: 用于数字图像自动竖直调整的技术和设备。 描述了自动立式调节技术，其可以提供用于矫正输入图像中的倾斜特征的自动化方法，以提高其感知质量。 这种校正可以被称为直立调节。 可以在直立式调整中使用基于人类感知的一组标准。 描述了实现优化框架的重投影技术，其基于标准产生用于调整的最佳单应性，并且根据由优化生成的新的相机参数来调整图像。 描述了基于优化的相机校准技术，其同时估计图像的消失线和点以及相机参数; 例如，校准技术可以用于生成输入到重投影技术的相机参数和消失点和线的估计。
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