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公开(公告)号：US20180286061A1
公开(公告)日：2018-10-04
申请号：US15996833
申请日：2018-06-04
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Scott Cohen , Peng Wang , Bryan Russell , Brian Price , Jonathan Eisenmann
IPC: G06T7/50
CPC classification number: G06T7/50 , G06T7/13 , G06T7/62 , G06T2207/20084
Abstract: Techniques for planar region-guided estimates of 3D geometry of objects depicted in a single 2D image. The techniques estimate regions of an image that are part of planar regions (i.e., flat surfaces) and use those planar region estimates to estimate the 3D geometry of the objects in the image. The planar regions and resulting 3D geometry are estimated using only a single 2D image of the objects. Training data from images of other objects is used to train a CNN with a model that is then used to make planar region estimates using a single 2D image. The planar region estimates, in one example, are based on estimates of planarity (surface plane information) and estimates of edges (depth discontinuities and edges between surface planes) that are estimated using models trained using images of other scenes.
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公开(公告)号：US09330334B2
公开(公告)日：2016-05-03
申请号：US14062559
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
CPC classification number: G06K9/4671 , G06T7/11 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/20004 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081
Abstract: In techniques for iterative saliency map estimation, a salient regions module applies a saliency estimation technique to compute a saliency map of an image that includes image regions. A salient image region of the image is determined from the saliency map, and an image region that corresponds to the salient image region is removed from the image. The salient regions module then iteratively determines subsequent salient image regions of the image utilizing the saliency estimation technique to recompute the saliency map of the image with the image region removed, and removes the image regions that correspond to the subsequent salient image regions from the image. The salient image regions of the image are iteratively determined until no salient image regions are detected in the image, and a salient features map is generated that includes each of the salient image regions determined iteratively and combined to generate the final saliency map.
Abstract translation: 在迭代显着性图估计技术中，显着区域模块应用显着性估计技术来计算包括图像区域的图像的显着性图。 从显着性图确定图像的显着图像区域，并且从图像中去除对应于显着图像区域的图像区域。 显着区域模块然后使用显着性估计技术迭代地确定图像的随后的显着图像区域，以重新计算去除图像区域的图像的显着图，并且从图像中去除与后续显着图像区域相对应的图像区域。 迭代地确定图像的显着图像区域，直到在图像中没有检测到显着的图像区域，并且生成包括迭代地确定并组合的每个显着图像区域以产生最终显着图的显着特征图。
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公开(公告)号：US20150116350A1
公开(公告)日：2015-04-30
申请号：US14062751
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
IPC: G06T3/40
CPC classification number: G06T11/60 , G06T3/0012 , G06T2207/20132
Abstract: In techniques of combined composition and change-based models for image cropping, a composition application is implemented to apply one or more image composition modules of a learned composition model to evaluate multiple composition regions of an image. The learned composition model can determine one or more cropped images from the image based on the applied image composition modules, and evaluate a composition of the cropped images and a validity of change from the image to the cropped images. The image composition modules of the learned composition model include a salient regions module that iteratively determines salient image regions of the image, and include a foreground detection module that determines foreground regions of the image. The image composition modules also include one or more imaging models that reduce a number of the composition regions of the image to facilitate determining the cropped images from the image.
Abstract translation: 在用于图像裁剪的组合和基于变化的组合模型的技术中，实施组合应用以应用学习的组合模型的一个或多个图像组合模块来评估图像的多个组合区域。 所学习的构图模型可以基于所应用的图像组合模块从图像中确定一个或多个裁剪图像，并且评估裁剪图像的组成以及从图像到裁剪图像的变化的有效性。 所学习的构图模型的图像合成模块包括迭代地确定图像的显着图像区域的显着区域模块，并且包括确定图像的前景区域的前景检测模块。 图像合成模块还包括减少图像的合成区域的数量的一个或多个成像模型，以便于从图像确定裁剪的图像。
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公开(公告)号：US10019823B2
公开(公告)日：2018-07-10
申请号：US14062751
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
CPC classification number: G06T11/60 , G06N7/005 , G06N20/10 , G06T3/0012 , G06T2207/20132
Abstract: In techniques of combined composition and change-based models for image cropping, a composition application is implemented to apply one or more image composition modules of a learned composition model to evaluate multiple composition regions of an image. The learned composition model can determine one or more cropped images from the image based on the applied image composition modules, and evaluate a composition of the cropped images and a validity of change from the image to the cropped images. The image composition modules of the learned composition model include a salient regions module that iteratively determines salient image regions of the image, and include a foreground detection module that determines foreground regions of the image. The image composition modules also include one or more imaging models that reduce a number of the composition regions of the image to facilitate determining the cropped images from the image.
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公开(公告)号：US09990728B2
公开(公告)日：2018-06-05
申请号：US15261749
申请日：2016-09-09
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Xiaohui Shen , Scott Cohen , Peng Wang , Bryan Russell , Brian Price , Jonathan Eisenmann
IPC: G06T7/00
CPC classification number: G06T7/50 , G06T7/0051 , G06T2207/20084
Abstract: Techniques for planar region-guided estimates of 3D geometry of objects depicted in a single 2D image. The techniques estimate regions of an image that are part of planar regions (i.e., flat surfaces) and use those planar region estimates to estimate the 3D geometry of the objects in the image. The planar regions and resulting 3D geometry are estimated using only a single 2D image of the objects. Training data from images of other objects is used to train a CNN with a model that is then used to make planar region estimates using a single 2D image. The planar region estimates, in one example, are based on estimates of planarity (surface plane information) and estimates of edges (depth discontinuities and edges between surface planes) that are estimated using models trained using images of other scenes.
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Patent Agency Ranking
公开(公告)号：US20160350930A1
公开(公告)日：2016-12-01
申请号：US14724660
申请日：2015-05-28
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Scott D. Cohen , Peng Wang , Xiaohui Shen , Brian L. Price
CPC classification number: G06K9/66 , G06K9/00664 , G06K9/4628 , G06K9/6272 , G06T7/50 , G06T2207/20081
Abstract: Joint depth estimation and semantic labeling techniques usable for processing of a single image are described. In one or more implementations, global semantic and depth layouts are estimated of a scene of the image through machine learning by the one or more computing devices. Local semantic and depth layouts are also estimated for respective ones of a plurality of segments of the scene of the image through machine learning by the one or more computing devices. The estimated global semantic and depth layouts are merged with the local semantic and depth layouts by the one or more computing devices to semantically label and assign a depth value to individual pixels in the image.
Abstract translation: 描述了可用于处理单个图像的联合深度估计和语义标注技术。 在一个或多个实现中，通过一个或多个计算设备的机器学习来估计图像的场景的全局语义和深度布局。 还通过一个或多个计算设备的机器学习来估计图像场景的多个片段中的各个片段的局部语义和深度布局。 估计的全局语义和深度布局由一个或多个计算设备与本地语义和深度布局合并，以语义地标记并分配图像中的各个像素的深度值。
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公开(公告)号：US10019657B2
公开(公告)日：2018-07-10
申请号：US14724660
申请日：2015-05-28
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Scott D. Cohen , Peng Wang , Xiaohui Shen , Brian L. Price
CPC classification number: G06K9/66 , G06K9/00664 , G06K9/4628 , G06K9/6272 , G06N3/0454 , G06N3/08 , G06N7/005 , G06N20/10 , G06T7/50 , G06T2207/20081
Abstract: Joint depth estimation and semantic labeling techniques usable for processing of a single image are described. In one or more implementations, global semantic and depth layouts are estimated of a scene of the image through machine learning by the one or more computing devices. Local semantic and depth layouts are also estimated for respective ones of a plurality of segments of the scene of the image through machine learning by the one or more computing devices. The estimated global semantic and depth layouts are merged with the local semantic and depth layouts by the one or more computing devices to semantically label and assign a depth value to individual pixels in the image.
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公开(公告)号：US20150117784A1
公开(公告)日：2015-04-30
申请号：US14062680
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
IPC: G06K9/46
CPC classification number: G06K9/4671 , G06T7/11 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/20004 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081
Abstract: In techniques for image foreground detection, a foreground detection module is implemented to generate varying levels of saliency thresholds from a saliency map of an image that includes foreground regions. The saliency thresholds can be generated based on an adaptive thresholding technique applied to the saliency map of the image and/or based on multi-level segmentation of the saliency map. The foreground detection module applies one or more constraints that distinguish the foreground regions in the image, and detects the foreground regions of the image based on the saliency thresholds and the constraints. Additionally, different ones of the constraints can be applied to detect different ones of the foreground regions, as well as to detect multi-level foreground regions based on the saliency thresholds and the constraints.
Abstract translation: 在用于图像前景检测的技术中，实施前景检测模块以从包括前景区域的图像的显着图生成不同级别的显着阈值。 可以基于应用于图像的显着图的自适应阈值技术和/或基于显着图的多级分割来生成显着阈值。 前景检测模块应用区分图像中的前景区域的一个或多个约束，并且基于显着性阈值和约束来检测图像的前景区域。 此外，可以应用不同的约束来检测不同的前景区域，以及基于显着性阈值和约束来检测多级前景区域。
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公开(公告)号：US09299004B2
公开(公告)日：2016-03-29
申请号：US14062680
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
CPC classification number: G06K9/4671 , G06T7/11 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/20004 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081
Abstract: In techniques for image foreground detection, a foreground detection module is implemented to generate varying levels of saliency thresholds from a saliency map of an image that includes foreground regions. The saliency thresholds can be generated based on an adaptive thresholding technique applied to the saliency map of the image and/or based on multi-level segmentation of the saliency map. The foreground detection module applies one or more constraints that distinguish the foreground regions in the image, and detects the foreground regions of the image based on the saliency thresholds and the constraints. Additionally, different ones of the constraints can be applied to detect different ones of the foreground regions, as well as to detect multi-level foreground regions based on the saliency thresholds and the constraints.
Abstract translation: 在用于图像前景检测的技术中，实施前景检测模块以从包括前景区域的图像的显着图生成不同级别的显着阈值。 可以基于应用于图像的显着图的自适应阈值技术和/或基于显着图的多级分割来生成显着阈值。 前景检测模块应用区分图像中的前景区域的一个或多个约束，并且基于显着性阈值和约束来检测图像的前景区域。 此外，可以应用不同的约束来检测不同的前景区域，以及基于显着性阈值和约束来检测多级前景区域。
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公开(公告)号：US20150117783A1
公开(公告)日：2015-04-30
申请号：US14062559
申请日：2013-10-24
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Peng Wang
IPC: G06K9/46
CPC classification number: G06K9/4671 , G06T7/11 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/20004 , G06T2207/20016 , G06T2207/20076 , G06T2207/20081
Abstract: In techniques for iterative saliency map estimation, a salient regions module applies a saliency estimation technique to compute a saliency map of an image that includes image regions. A salient image region of the image is determined from the saliency map, and an image region that corresponds to the salient image region is removed from the image. The salient regions module then iteratively determines subsequent salient image regions of the image utilizing the saliency estimation technique to recompute the saliency map of the image with the image region removed, and removes the image regions that correspond to the subsequent salient image regions from the image. The salient image regions of the image are iteratively determined until no salient image regions are detected in the image, and a salient features map is generated that includes each of the salient image regions determined iteratively and combined to generate the final saliency map.
Abstract translation: 在迭代显着性图估计技术中，显着区域模块应用显着性估计技术来计算包括图像区域的图像的显着性图。 从显着性图确定图像的显着图像区域，并且从图像中去除对应于显着图像区域的图像区域。 显着区域模块然后使用显着性估计技术迭代地确定图像的随后的显着图像区域，以重新计算去除图像区域的图像的显着图，并且从图像中去除与后续显着图像区域相对应的图像区域。 迭代地确定图像的显着图像区域，直到在图像中没有检测到显着的图像区域，并且生成包括迭代地确定并组合的每个显着图像区域以产生最终显着图的显着特征图。
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