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公开(公告)号：US20150154743A1
公开(公告)日：2015-06-04
申请号：US14615029
申请日：2015-02-05
Applicant: Google Inc.
Inventor： Sergey Ioffe , Troy Chinen , Vivek Kwatra , Hui Fang , Yichang Shih
CPC classification number: G06T5/002 , G06K9/00221 , G06K9/00261 , G06K9/40 , G06T5/005 , G06T5/20 , G06T5/50 , G06T2207/10004 , G06T2207/10016 , G06T2207/20028 , G06T2207/20182 , G06T2207/20216 , G06T2207/30201
Abstract: A method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.
Abstract translation: 一种用于识别第一图像中的面部图像的第一部分的方法，计算机程序产品和计算机系统，其中所述第一部分包括噪声。 面部图像的相应部分在第二图像中被识别，其中对应部分包括比第一部分少的噪声。 至少部分地基于第一部分和对应部分来确定第一部分的一个或多个过滤器参数。 至少部分地基于一个或多个过滤器参数来平滑来自第一部分的噪声的至少一部分。 来自相应部分的面部特定细节的至少一部分被添加到第一部分。
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公开(公告)号：US09659352B2
公开(公告)日：2017-05-23
申请号：US14615029
申请日：2015-02-05
Applicant: Google Inc.
Inventor： Sergey Ioffe , Troy Chinen , Vivek Kwatra , Hui Fang , Yichang Shih
CPC classification number: G06T5/002 , G06K9/00221 , G06K9/00261 , G06K9/40 , G06T5/005 , G06T5/20 , G06T5/50 , G06T2207/10004 , G06T2207/10016 , G06T2207/20028 , G06T2207/20182 , G06T2207/20216 , G06T2207/30201
Abstract: A method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.
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Patent Agency Ranking
公开(公告)号：US08983193B1
公开(公告)日：2015-03-17
申请号：US13628735
申请日：2012-09-27
Applicant: Google Inc.
Inventor： Vicente Ignacio Ordonez Roman , Jennifer Ann Gillenwater , Rodrigo Carceroni , Amarnag Subramanya , Wei Hua , Hui Fang
CPC classification number: G06K9/00 , G06F17/30247 , G06K9/00684 , G06K9/6263 , G06K9/6277
Abstract: A computer-implemented technique can receive, at a computing device including one or more processors, a plurality of photos. The technique can extract quality features and similarity features for each of the plurality of photos and can obtain weights for the various quality features and similarity features based on an analysis of a reference photo collection. The technique can generate a quality metric for each of the plurality of photos and can generate a similarity matrix for the plurality of photos by analyzing the various quality features and similarity features and using the obtained weights. The technique can perform joint global maximization of photo quality and photo diversity using the quality metrics and the similarity matrix in order to select a subset of the plurality of photos having a high degree of representativeness. The technique can then store the subset of the plurality of photos in a memory.
Abstract translation: 计算机实现的技术可以在包括一个或多个处理器的计算设备处接收多个照片。 该技术可以为多张照片中的每一张照片提取质量特征和相似性特征，并且可以基于参考照片集合的分析来获得各种质量特征和相似性特征的权重。 该技术可以为多个照片中的每一个生成质量度量，并且可以通过分析各种质量特征和相似性特征并使用获得的权重来生成多张照片的相似性矩阵。 该技术可以使用质量度量和相似性矩阵来执行照片质量和照片分集的联合全局最大化，以便选择具有高度代表性的多张照片的子集。 该技术然后可以将多个照片的子集存储在存储器中。
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公开(公告)号：US20140119664A1
公开(公告)日：2014-05-01
申请号：US13665449
申请日：2012-10-31
Applicant: Google Inc.
Inventor： Sergey Ioffe , Troy Chinen , Vivek Kwatra , Hui Fang , Yichang Shih
IPC: G06K9/40
CPC classification number: G06T5/002 , G06K9/00221 , G06K9/00261 , G06K9/40 , G06T5/005 , G06T5/20 , G06T5/50 , G06T2207/10004 , G06T2207/10016 , G06T2207/20028 , G06T2207/20182 , G06T2207/20216 , G06T2207/30201
Abstract: A method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.
Abstract translation: 一种用于识别第一图像中的面部图像的第一部分的方法，计算机程序产品和计算机系统，其中所述第一部分包括噪声。 面部图像的相应部分在第二图像中被识别，其中对应部分包括比第一部分少的噪声。 至少部分地基于第一部分和对应部分来确定第一部分的一个或多个过滤器参数。 至少部分地基于一个或多个过滤器参数来平滑来自第一部分的噪声的至少一部分。 来自相应部分的面部特定细节的至少一部分被添加到第一部分。
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公开(公告)号：US08977012B2
公开(公告)日：2015-03-10
申请号：US13665449
申请日：2012-10-31
Applicant: Google Inc.
Inventor： Sergey Ioffe , Troy Chinen , Vivek Kwatra , Hui Fang , Yichang Shih
CPC classification number: G06T5/002 , G06K9/00221 , G06K9/00261 , G06K9/40 , G06T5/005 , G06T5/20 , G06T5/50 , G06T2207/10004 , G06T2207/10016 , G06T2207/20028 , G06T2207/20182 , G06T2207/20216 , G06T2207/30201
Abstract: A method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.
Abstract translation: 一种用于识别第一图像中的面部图像的第一部分的方法，计算机程序产品和计算机系统，其中所述第一部分包括噪声。 面部图像的相应部分在第二图像中被识别，其中对应部分包括比第一部分少的噪声。 至少部分地基于第一部分和对应部分来确定第一部分的一个或多个过滤器参数。 至少部分地基于一个或多个过滤器参数来平滑来自第一部分的噪声的至少一部分。 来自相应部分的面部特定细节的至少一部分被添加到第一部分。
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公开(公告)号：US09036905B2
公开(公告)日：2015-05-19
申请号：US14483422
申请日：2014-09-11
Applicant: Google Inc.
Inventor： Hui Fang
CPC classification number: G06T5/003 , G06K9/4609 , G06K9/6256 , G06K9/627 , G06T5/10 , G06T2207/20056 , G06T2207/20081 , G06T2207/20201
Abstract: A classifier training system trains a classifier for evaluating image deblurring quality using a set of scored deblurred images. In some embodiments, the classifier training system trains the classifier based on a number of sub-images extracted from the scored deblurred images. An image deblurring system applies a number of different deblurring transformations to a given blurry reference image and uses the classifier trained by the classifier training system to evaluate deblurring quality, thereby finding a highest-quality deblurred image. In some embodiments, the classifier training system trains the classifier in the frequency domain, and the image deblurring system uses the classifier trained by the classifier training system to evaluate deblurring quality in the frequency domain. In some embodiments, the image deblurring system applies the different deblurring transformations iteratively.
Abstract translation: 分类器训练系统使用一组得分的去模糊图像来训练用于评估图像去模糊质量的分类器。 在一些实施例中，分类器训练系统基于从得分的去模糊图像提取的多个子图像来训练分类器。 图像去模糊系统将一些不同的去模糊变换应用于给定的模糊参考图像，并使用由分类器训练系统训练的分类器来评估去模糊质量，从而找到最高质量的去模糊图像。 在一些实施例中，分类器训练系统在频域中训练分类器，并且图像去模糊系统使用由分类器训练系统训练的分类器来评估频域中的去保护质量。 在一些实施例中，图像去模糊系统迭代地应用不同的去模糊变换。
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公开(公告)号：US08861884B1
公开(公告)日：2014-10-14
申请号：US13682637
申请日：2012-11-20
Applicant: Google Inc.
Inventor： Hui Fang
CPC classification number: G06T5/003 , G06K9/4609 , G06K9/6256 , G06K9/627 , G06T5/10 , G06T2207/20056 , G06T2207/20081 , G06T2207/20201
Abstract: A classifier training system trains a classifier for evaluating image deblurring quality using a set of scored deblurred images. In some embodiments, the classifier training system trains the classifier based on a number of sub-images extracted from the scored deblurred images. An image deblurring system applies a number of different deblurring transformations to a given blurry reference image and uses the classifier trained by the classifier training system to evaluate deblurring quality, thereby finding a highest-quality deblurred image. In some embodiments, the classifier training system trains the classifier in the frequency domain, and the image deblurring system uses the classifier trained by the classifier training system to evaluate deblurring quality in the frequency domain. In some embodiments, the image deblurring system applies the different deblurring transformations iteratively.
Abstract translation: 分类器训练系统使用一组得分的去模糊图像来训练用于评估图像去模糊质量的分类器。 在一些实施例中，分类器训练系统基于从得分的去模糊图像提取的多个子图像来训练分类器。 图像去模糊系统将一些不同的去模糊变换应用于给定的模糊参考图像，并使用由分类器训练系统训练的分类器来评估去模糊质量，从而找到最高质量的去模糊图像。 在一些实施例中，分类器训练系统在频域中训练分类器，并且图像去模糊系统使用由分类器训练系统训练的分类器来评估频域中的去保护质量。 在一些实施例中，图像去模糊系统迭代地应用不同的去模糊变换。
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