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公开(公告)号：US20160275341A1
公开(公告)日：2016-09-22
申请号：US14661788
申请日：2015-03-18
Applicant: Adobe Systems Incorporated
Inventor： Wilmot Wei-Mau Li , Jianchao Yang , Linjie Luo , Jonathan W. Brandt , Xiang Yu
CPC classification number: G06K9/00288 , G06K9/00228 , G06K9/00281 , G06K9/00302 , G06K9/00315 , G06K9/4604 , G06K9/4628 , G06K9/4642 , G06K9/6269 , G06K9/6272 , G06K9/66 , G06T7/246 , G06T13/40
Abstract: Techniques for facial expression capture for character animation are described. In one or more implementations, facial key points are identified in a series of images. Each image, in the series of images, is normalized from the identified facial key points. Facial features are determined from each of the normalized images. Then a facial expression is classified, based on the determined facial features, for each of the normalized images. In additional implementations, a series of images are captured that include performances of one or more facial expressions. The facial expressions in each image of the series of images are classified by a facial expression classifier. Then the facial expression classifications are used by a character animator system to produce a series of animated images of an animated character that include animated facial expressions that are associated with the facial expression classification of the corresponding image in the series of images.
Abstract translation: 描述了用于人物动画的面部表情捕获的技术。 在一个或多个实现中，在一系列图像中识别面部关键点。 一系列图像中的每个图像都从识别的面部关键点进行归一化。 从每个标准化图像确定面部特征。 然后，基于所确定的面部特征，针对每个标准化图像分类面部表情。 在另外的实现中，捕获包括一个或多个面部表情的表现的一系列图像。 一系列图像的每个图像中的面部表情由面部表情分类器分类。 然后，人物动画师系统使用面部表情分类来产生动画角色的一系列动画图像，其包括与一系列图像中的对应图像的面部表情分类相关联的动画面部表情。
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公开(公告)号：US20160148079A1
公开(公告)日：2016-05-26
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：US20160019440A1
公开(公告)日：2016-01-21
申请号：US14335059
申请日：2014-07-18
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/6256 , G06F17/30247 , G06F17/30253 , G06K9/46 , G06K9/4676 , G06K9/469 , G06N5/04 , G06N99/005
Abstract: Feature interpolation techniques are described. In a training stage, features are extracted from a collection of training images and quantized into visual words. Spatial configurations of the visual words in the training images are determined and stored in a spatial configuration database. In an object detection stage, a portion of features of an image are extracted from the image and quantized into visual words. Then, a remaining portion of the features of the image are interpolated using the visual words and the spatial configurations of visual words stored in the spatial configuration database.
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公开(公告)号：US20140244669A1
公开(公告)日：2014-08-28
申请号：US13778938
申请日：2013-02-27
Applicant: Adobe Systems Incorporated
Inventor： Jonathan W. Brandt , Nico A. Becherer , Sven Duwenhorst
IPC: G06F17/30
CPC classification number: G06F17/3025 , G06K9/4652
Abstract: Each of multiple images is analyzed to determine how the colors of the pixels of the image are distributed throughout the color space of the image. Different covariance based characteristics of the image are determined that identify a direction, as well as magnitude in each direction, of the distribution of colors of the image pixels. These different covariance based characteristics that are determined for an image can be saved as associated with the image, allowing the characteristics to be accessed and used as a basis for searching the images to identify particular types of images. These different covariance based characteristics can also be used to order the images identified by a search.
Abstract translation: 分析多个图像中的每一个以确定图像的像素的颜色如何分布在图像的整个颜色空间中。 确定图像的不同协方差特征，其识别图像像素的颜色分布的方向以及每个方向上的大小。 为图像确定的这些不同的协方差特征可以被保存为与图像相关联，允许访问特征并将其用作搜索图像以识别特定类型的图像的基础。 这些不同的协方差特征也可以用于对通过搜索识别的图像进行排序。
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公开(公告)号：US09818044B2
公开(公告)日：2017-11-14
申请号：US14938781
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
CPC classification number: G06K9/6202 , G06K9/6201 , G06K9/80 , G06T11/60 , G06T2200/24
Abstract: Content update and suggestion techniques are described. In one or more implementations, techniques are implemented to generate suggestions that are usable to guide creative professionals in updating content such as images, video, sound, multimedia, and so forth. A variety of techniques are usable to generate suggestions for the content professionals. In one example, suggestions are based on shared characteristics of images licensed by users of a content sharing service, e.g., licensed by the users. In another example, suggestions are based on metadata of the images licensed by the users, the metadata describing characteristics of how the images are created. These suggestions are then used to guide transformation of a user's image such that the image exhibits these characteristics and thus has an increased likelihood of being desired for licensing by customers of the service.
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Patent Agency Ranking
公开(公告)号：US20170132490A1
公开(公告)日：2017-05-11
申请号：US14938781
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
CPC classification number: G06K9/6202 , G06K9/6201 , G06K9/80 , G06T11/60 , G06T2200/24
Abstract: Content update and suggestion techniques are described. In one or more implementations, techniques are implemented to generate suggestions that are usable to guide creative professionals in updating content such as images, video, sound, multimedia, and so forth. A variety of techniques are usable to generate suggestions for the content professionals. In one example, suggestions are based on shared characteristics of images licensed by users of a content sharing service, e.g., licensed by the users. In another example, suggestions are based on metadata of the images licensed by the users, the metadata describing characteristics of how the images are created. These suggestions are then used to guide transformation of a user's image such that the image exhibits these characteristics and thus has an increased likelihood of being desired for licensing by customers of the service.
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公开(公告)号：US20170116467A1
公开(公告)日：2017-04-27
申请号：US15398881
申请日：2017-01-05
Applicant: Adobe Systems Incorporated
Inventor： Wilmot Wei-Mau Li , Jianchao Yang , Linjie Luo , Jonathan W. Brandt , Xiang Yu
CPC classification number: G06K9/00288 , G06K9/00228 , G06K9/00281 , G06K9/00302 , G06K9/00315 , G06K9/4604 , G06K9/4628 , G06K9/4642 , G06K9/6269 , G06K9/6272 , G06K9/66 , G06T7/246 , G06T13/40
Abstract: Techniques for facial expression capture for character animation are described. In one or more implementations, facial key points are identified in a series of images. Each image, in the series of images, is normalized from the identified facial key points. Facial features are determined from each of the normalized images. Then a facial expression is classified, based on the determined facial features, for each of the normalized images. In additional implementations, a series of images are captured that include performances of one or more facial expressions. The facial expressions in each image of the series of images are classified by a facial expression classifier. Then the facial expression classifications are used by a character animator system to produce a series of animated images of an animated character that include animated facial expressions that are associated with the facial expression classification of the corresponding image in the series of images.
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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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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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