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公开(公告)号：US09672414B2
公开(公告)日：2017-06-06
申请号：US14938568
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Kalyan K. Sunkavalli , Zhe Lin , Xiaohui Shen , Joon-Young Lee
CPC classification number: G06K9/00288 , G06K9/00228 , G06K9/00234 , G06T5/001 , G06T5/008 , G06T2207/10024 , G06T2207/20032 , G06T2207/20208 , G06T2207/30201
Abstract: An image processing application performs improved face exposure correction on an input image. The image processing application receives an input image having a face and ascertains a median luminance associated with a face region corresponding to the face. The image processing application determines whether the median luminance is less than a threshold luminance. If the median luminance is less than the threshold luminance, the application computes weights based on a spatial distance parameter and a similarity parameter associated with the median chrominance of the face region. The image processing application then computes a corrected luminance using the weights and applies the corrected luminance to the input image. The image processing application can also perform improved face color correction by utilizing stylization-induced shifts in skin tone color to control how aggressively stylization is applied to an image.
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公开(公告)号：US20170140213A1
公开(公告)日：2017-05-18
申请号：US14945198
申请日：2015-11-18
Applicant: Adobe Systems Incorporated
Inventor： Jonathan Brandt , Zhe Lin , Xiaohui Shen , Haoxiang Li
CPC classification number: G06K9/00295 , G06K9/00369 , G06K9/00677 , G06K9/6218 , G06K9/66
Abstract: Methods and systems for recognizing people in images with increased accuracy are disclosed. In particular, the methods and systems divide images into a plurality of clusters based on common characteristics of the images. The methods and systems also determine an image cluster to which an image with an unknown person instance most corresponds. One or more embodiments determine a probability that the unknown person instance is each known person instance in the image cluster using a trained cluster classifier of the image cluster. Optionally, the methods and systems determine context weights for each combination of an unknown person instance and each known person instance using a conditional random field algorithm based on a plurality of context cues associated with the unknown person instance and the known person instances. The methods and systems calculate a contextual probability based on the cluster-based probabilities and context weights to identify the unknown person instance.
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公开(公告)号：US09626584B2
公开(公告)日：2017-04-18
申请号：US14511001
申请日：2014-10-09
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
CPC classification number: G06T3/40 , G06K9/4671 , G06T3/0012 , G06T11/60 , G06T2210/22
Abstract: Image cropping suggestion using multiple saliency maps is described. In one or more implementations, component scores, indicative of visual characteristics established for visually-pleasing croppings, are computed for candidate image croppings using multiple different saliency maps. The visual characteristics on which a candidate image cropping is scored may be indicative of its composition quality, an extent to which it preserves content appearing in the scene, and a simplicity of its boundary. Based on the component scores, the croppings may be ranked with regard to each of the visual characteristics. The rankings may be used to cluster the candidate croppings into groups of similar croppings, such that croppings in a group are different by less than a threshold amount and croppings in different groups are different by at least the threshold amount. Based on the clustering, croppings may then be chosen, e.g., to present them to a user for selection.
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公开(公告)号：US20160371538A1
公开(公告)日：2016-12-22
申请号：US15254587
申请日：2016-09-01
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Zhe Lin , Jonathan W. Brandt
IPC: G06K9/00
CPC classification number: G06K9/00268 , G06K9/00228 , G06K9/00281 , G06K9/4676 , G06K9/623 , G06K9/6257
Abstract: Accelerating object detection techniques are described. In one or more implementations, adaptive sampling techniques are used to extract features from an image. Coarse features are extracted from the image and used to generate an object probability map. Then, dense features are extracted from high-probability object regions of the image identified in the object probability map to enable detection of an object in the image. In one or more implementations, cascade object detection techniques are used to detect an object in an image. In a first stage, exemplars in a first subset of exemplars are applied to features extracted from the multiple regions of the image to detect object candidate regions. Then, in one or more validation stages, the object candidate regions are validated by applying exemplars from the first subset of exemplars and one or more additional subsets of exemplars.
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公开(公告)号：US20160307074A1
公开(公告)日：2016-10-20
申请号：US15196478
申请日：2016-06-29
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Haoxiang Li , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/00288 , G06K9/4628 , G06K9/6257 , G06N3/0454
Abstract: Different candidate windows in an image are identified, such as by sliding a rectangular or other geometric shape of different sizes over an image to identify portions of the image (groups of pixels in the image). The candidate windows are analyzed by a set of convolutional neural networks, which are cascaded so that the input of one convolutional neural network layer is based on the input of another convolutional neural network layer. Each convolutional neural network layer drops or rejects one or more candidate windows that the convolutional neural network layer determines does not include an object (e.g., a face). The candidate windows that are identified as including an object (e.g., a face) are analyzed by another one of the convolutional neural network layers. The candidate windows identified by the last of the convolutional neural network layers are the indications of the objects (e.g., faces) in the image.
Abstract translation: 识别图像中的不同候选窗口，例如通过在图像上滑动不同尺寸的矩形或其他几何形状以识别图像（图像中的像素组）的部分。 候选窗口由一组卷积神经网络进行分析，这些网络级联，使得一个卷积神经网络层的输入基于另一个卷积神经网络层的输入。 每个卷积神经网络层丢弃或拒绝卷积神经网络层确定的一个或多个候选窗口不包括对象（例如，面部）。 识别为包括对象（例如脸部）的候选窗口由另一个卷积神经网络层分析。 由最后的卷积神经网络层识别的候选窗口是图像中的对象（例如，面部）的指示。
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Patent Agency Ranking
公开(公告)号：US09471828B2
公开(公告)日：2016-10-18
申请号：US14444560
申请日：2014-07-28
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/00268 , G06K9/00228 , G06K9/00281 , G06K9/4676 , G06K9/623 , G06K9/6257
Abstract: Accelerating object detection techniques are described. In one or more implementations, adaptive sampling techniques are used to extract features from an image. Coarse features are extracted from the image and used to generate an object probability map. Then, dense features are extracted from high-probability object regions of the image identified in the object probability map to enable detection of an object in the image. In one or more implementations, cascade object detection techniques are used to detect an object in an image. In a first stage, exemplars in a first subset of exemplars are applied to features extracted from the multiple regions of the image to detect object candidate regions. Then, in one or more validation stages, the object candidate regions are validated by applying exemplars from the first subset of exemplars and one or more additional subsets of exemplars.
Abstract translation: 描述加速对象检测技术。 在一个或多个实现中，使用自适应采样技术来从图像中提取特征。 从图像中提取粗略特征，并用于生成目标概率图。 然后，从在目标概率图中识别的图像的高概率对象区域提取密集特征，以使得能够检测图像中的对象。 在一个或多个实现中，使用级联对象检测技术来检测图像中的对象。 在第一阶段，样本的第一子集中的样本被应用于从图像的多个区域提取的特征以检测对象候选区域。 然后，在一个或多个验证阶段中，通过应用示例的第一子集和示例的一个或多个附加子集来验证对象候选区域。
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公开(公告)号：US20160300331A1
公开(公告)日：2016-10-13
申请号：US15187924
申请日：2016-06-21
Applicant: Adobe Systems Incorporated
Inventor： Chih-Yao Hsieh , Zhe Lin
CPC classification number: G06T5/003 , G06T3/4053 , G06T3/4076 , G06T5/00 , G06T5/001 , G06T5/002 , G06T5/20 , G06T11/00 , G06T11/001 , G06T11/60 , G06T2207/20021 , G06T2207/20024
Abstract: Techniques for enhancing an image using pixel-specific processing are disclosed. An image can be enhanced by updating certain pixels through patch aggregation. Neighboring pixels of a selected pixel are identified. Respective patch values for patches containing the selected pixel are determined. Patch values provide update information for updating the respective pixels in the patch. Relevant patch values for the selected pixel are identified by identifying associated patches of the pixel. Information from the relevant patch values of the selected pixel may be obtained. Using this information, pixel-specific processing may be performed to determine an updated pixel value for the selected pixel or for neighboring pixels of the selected pixel. Pixel-specific processes may be executed for each of the selected or neighboring pixels. These pixel-specific processes can be executed in parallel. Therefore, through the execution of pixel-specific processes, which may be performed concurrently, an enhanced image may be determined.
Abstract translation: 公开了使用像素特定处理来增强图像的技术。 通过补丁聚合更新某些像素可以增强图像。 识别所选像素的相邻像素。 确定包含所选像素的补丁的各个补丁值。 补丁值提供更新补丁中各个像素的更新信息。 通过识别像素的相关补丁来识别所选像素的相关补丁值。 可以获得来自所选像素的相关补丁值的信息。 使用该信息，可以执行像素特定处理以确定所选择的像素或所选像素的相邻像素的更新的像素值。 可以针对所选择的或相邻像素中的每一个执行像素特定的处理。 这些像素特定的处理可以并行执行。 因此，通过执行可以同时执行的像素特定处理，可以确定增强图像。
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公开(公告)号：US09418319B2
公开(公告)日：2016-08-16
申请号：US14550800
申请日：2014-11-21
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Haoxiang Li , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/00288 , G06K9/4628 , G06K9/6257 , G06N3/0454
Abstract: Different candidate windows in an image are identified, such as by sliding a rectangular or other geometric shape of different sizes over an image to identify portions of the image (groups of pixels in the image). The candidate windows are analyzed by a set of convolutional neural networks, which are cascaded so that the input of one convolutional neural network layer is based on the input of another convolutional neural network layer. Each convolutional neural network layer drops or rejects one or more candidate windows that the convolutional neural network layer determines does not include an object (e.g., a face). The candidate windows that are identified as including an object (e.g., a face) are analyzed by another one of the convolutional neural network layers. The candidate windows identified by the last of the convolutional neural network layers are the indications of the objects (e.g., faces) in the image.
Abstract translation: 识别图像中的不同候选窗口，例如通过在图像上滑动不同尺寸的矩形或其他几何形状以识别图像（图像中的像素组）的部分。 候选窗口由一组卷积神经网络进行分析，这些网络级联，使得一个卷积神经网络层的输入基于另一个卷积神经网络层的输入。 每个卷积神经网络层丢弃或拒绝卷积神经网络层确定的一个或多个候选窗口不包括对象（例如，面部）。 识别为包括对象（例如脸部）的候选窗口由另一个卷积神经网络层分析。 由最后的卷积神经网络层识别的候选窗口是图像中的对象（例如，面部）的指示。
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公开(公告)号：US20160179844A1
公开(公告)日：2016-06-23
申请号：US14573963
申请日：2014-12-17
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Xin Lu , Zhe Lin , Radomir Mech
CPC classification number: G06F17/30256 , G06F17/30247 , G06F17/30271 , G06K9/00221 , G06K9/00261 , G06K9/036 , G06K9/4628 , G06K9/6215 , G06K9/6227 , G06K9/6272 , G06K9/66 , G06N3/04 , G06N3/08
Abstract: Neural network image curation techniques are described. In one or more implementations, curation is controlled of images that represent a repository of images. A plurality of images of the repository are curated by one or more computing devices to select representative images of the repository. The curation includes calculating a score based on image and face aesthetics, jointly, for each of the plurality of images through processing by a neural network, ranking the plurality of images based on respective said scores, and selecting one or more of the plurality of images as one of the representative images of the repository based on the ranking and a determination that the one or more said images are not visually similar to images that have already been selected as one of the representative images of the repository.
Abstract translation: 描述神经网络图像策划技术。 在一个或多个实现中，控制图像的图像的图像库。 存储库的多个图像由一个或多个计算设备进行策划，以选择存储库的代表图像。 该策展包括基于图像和面部美学计算一个分数，通过神经网络的处理来共同地为多个图像中的每个图像，基于相应的分数对多个图像进行排序，并且选择多个图像中的一个或多个 作为基于排名的存储库的代表性图像之一，并且确定一个或多个所述图像在视觉上与已经被选择为存储库的代表图像之一的图像相似。
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公开(公告)号：US09367766B2
公开(公告)日：2016-06-14
申请号：US14338216
申请日：2014-07-22
Applicant: Adobe Systems Incorporated
Inventor： Jue Wang , Jianchao Yang , Zhe Lin , Ke Ma
CPC classification number: G06K9/6261 , G06K9/00442 , G06K9/00449 , G06K9/00463 , G06K9/00818 , G06K9/3258 , G06K9/72 , G06T7/11 , H04N1/00331
Abstract: Techniques for detecting and recognizing text may be provided. For example, an image may be analyzed to detect and recognize text therein. The analysis may involve detecting text components in the image. For example, multiple color spaces and multiple-stage filtering may be applied to detect the text components. Further, the analysis may involve extracting text lines based on the text components. For example, global information about the text components can be analyzed to generate best-fitting text lines. The analysis may also involve pruning and splitting the text lines to generate bounding boxes around groups of text components. Text recognition may be applied to the bounding boxes to recognize text therein.
Abstract translation: 可以提供用于检测和识别文本的技术。 例如，可以分析图像以检测和识别其中的文本。 分析可能涉及检测图像中的文本成分。 例如，可以应用多个颜色空间和多级过滤来检测文本分量。 此外，分析可以涉及基于文本成分提取文本行。 例如，可以分析关于文本组件的全局信息，以生成最合适的文本行。 分析还可能涉及修剪和分割文本行以在文本组件组周围生成边界框。 可以将文本识别应用于边界框以识别其中的文本。
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