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公开(公告)号：US20150139538A1
公开(公告)日：2015-05-21
申请号：US14081489
申请日：2013-11-15
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan W. Brandt , Xiaohui Shen , Haoxiang Li
CPC classification number: G06K9/6269 , G06K9/00234 , G06K9/00288 , G06K9/6257
Abstract: In techniques for object detection with boosted exemplars, weak classifiers of a real-adaboost technique can be learned as exemplars that are collected from example images. The exemplars are examples of an object that is detectable in image patches of an image, such as faces that are detectable in images. The weak classifiers of the real-adaboost technique can be applied to the image patches of the image, and a confidence score is determined for each of the weak classifiers as applied to an image patch of the image. The confidence score of a weak classifier is an indication of whether the object is detected in the image patch of the image based on the weak classifier. All of the confidence scores of the weak classifiers can then be summed to generate an overall object detection score that indicates whether the image patch of the image includes the object.
Abstract translation: 在通过增强的样本进行物体检测的技术中，可以从实例图像中收集真实adaboost技术的弱分类器作为样本。 示例是在图像的图像块中可检测到的对象的示例，例如在图像中可检测的面。 真实adaboost技术的弱分类器可以应用于图像的图像斑块，并且对于每个弱分类器确定应用于图像的图像块的置信度分数。 弱分类器的置信度分数是基于弱分类器在图像的图像块中是否检测到对象的指示。 然后可以将弱分类器的所有置信分数相加以生成指示图像的图像块是否包括对象的整体对象检测分数。
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公开(公告)号：US20140099031A1
公开(公告)日：2014-04-10
申请号：US13645463
申请日：2012-10-04
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Jonathan W. Brandt , Zhe Lin , Vuong Le , Lubomir D. Bourdev
IPC: G06K9/46
CPC classification number: G06K9/6209 , G06K9/00228 , G06K9/2081 , G06K2009/366
Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. A profile model and a shape model may be applied to an object in an image to determine locations of feature points for each object component. Input may be received to move one of the feature points to a fixed location. Other ones of the feature points may be automatically adjusted to different locations based on the moved feature point.
Abstract translation: 公开了用于特征点定位的方法和装置的各种实施例。 轮廓模型和形状模型可以应用于图像中的对象，以确定每个对象分量的特征点的位置。 可以接收输入以将特征点中的一个移动到固定位置。 其他特征点可以根据所移动的特征点自动调整到不同的位置。
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公开(公告)号：US09734434B2
公开(公告)日：2017-08-15
申请号：US15183629
申请日：2016-06-15
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：US20170131877A1
公开(公告)日：2017-05-11
申请号：US14938660
申请日：2015-11-11
Applicant: Adobe Systems Incorporated
Inventor： Zeke Koch , Gavin Stuart Peter Miller , Jonathan W. Brandt , Nathan A. Carr , Radomir Mech , Walter Wei-Tuh Chang , Scott D. Cohen , Hailin Jin
CPC classification number: H04L67/1074 , G06F21/10 , G06F21/6218 , G06Q20/1235 , G06T11/001 , G06T11/60 , G06T2200/24
Abstract: Content creation and sharing integration techniques and systems are described. In one or more implementations, techniques are described in which modifiable versions of content (e.g., images) are created and shared via a content sharing service such that image creation functionality used to create the images is preserved to permit continued creation using this functionality. In one or more additional implementations, image creation functionality employed by a creative professional to create content is leveraged to locate similar images from a content sharing service.
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公开(公告)号：US09552510B2
公开(公告)日：2017-01-24
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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Patent Agency Ranking
公开(公告)号：US20160148078A1
公开(公告)日：2016-05-26
申请号：US14549350
申请日：2014-11-20
Applicant: Adobe Systems Incorporated
Inventor： Xiaohui Shen , Haoxiang Li , Zhe Lin , Jonathan W. Brandt
CPC classification number: G06K9/66 , G06K9/4628 , G06K9/6267 , G06K9/6272 , G06N3/04 , G06N3/0454
Abstract: A convolutional neural network is trained to analyze input data in various different manners. The convolutional neural network includes multiple layers, one of which is a convolution layer that performs a convolution, for each of one or more filters in the convolution layer, of the filter over the input data. The convolution includes generation of an inner product based on the filter and the input data. Both the filter of the convolution layer and the input data are binarized, allowing the inner product to be computed using particular operations that are typically faster than multiplication of floating point values. The possible results for the convolution layer can optionally be pre-computed and stored in a look-up table. Thus, during operation of the convolutional neural network, rather than performing the convolution on the input data, the pre-computed result can be obtained from the look-up table
Abstract translation: 训练卷积神经网络以各种不同的方式分析输入数据。 卷积神经网络包括多个层，其中之一是卷积层，其对于卷积层中的一个或多个滤波器的每一个，通过输入数据执行滤波器的卷积。 卷积包括基于滤波器和输入数据生成内积。 卷积层的滤波器和输入数据都被二值化，允许使用通常快于浮点值乘法的特定运算来计算内积。 可以可选地预先计算卷积层的可能结果并将其存储在查找表中。 因此，在卷积神经网络的操作期间，不是对输入数据执行卷积，所以可以从查找表中获得预先计算的结果
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公开(公告)号：US20160027181A1
公开(公告)日：2016-01-28
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：US09208404B2
公开(公告)日：2015-12-08
申请号：US14081489
申请日：2013-11-15
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jonathan W. Brandt , Xiaohui Shen , Haoxiang Li
CPC classification number: G06K9/6269 , G06K9/00234 , G06K9/00288 , G06K9/6257
Abstract: In techniques for object detection with boosted exemplars, weak classifiers of a real-adaboost technique can be learned as exemplars that are collected from example images. The exemplars are examples of an object that is detectable in image patches of an image, such as faces that are detectable in images. The weak classifiers of the real-adaboost technique can be applied to the image patches of the image, and a confidence score is determined for each of the weak classifiers as applied to an image patch of the image. The confidence score of a weak classifier is an indication of whether the object is detected in the image patch of the image based on the weak classifier. All of the confidence scores of the weak classifiers can then be summed to generate an overall object detection score that indicates whether the image patch of the image includes the object.
Abstract translation: 在通过增强的样本进行物体检测的技术中，可以从实例图像中收集真实adaboost技术的弱分类器作为样本。 示例是在图像的图像块中可检测到的对象的示例，例如在图像中可检测的面。 真实adaboost技术的弱分类器可以应用于图像的图像斑块，并且对于每个弱分类器确定应用于图像的图像块的置信度分数。 弱分类器的置信度分数是基于弱分类器在图像的图像块中是否检测到对象的指示。 然后可以将弱分类器的所有置信分数相加以生成指示图像的图像块是否包括对象的整体对象检测分数。
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公开(公告)号：US09158963B2
公开(公告)日：2015-10-13
申请号：US13645453
申请日：2012-10-04
Applicant: Adobe Systems Incorporated
Inventor： Jonathan W. Brandt , Zhe Lin , Lubomir D. Bourdev , Vuong Le
CPC classification number: G06K9/00281 , G06K9/6209
Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. An object in an input image may be detected. A profile model may be applied to determine feature point locations for each object component of the detected object. Applying the profile model may include globally optimizing the feature points for each object component to find a global energy minimum. A component-based shape model may be applied to update the respective feature point locations for each object component.
Abstract translation: 公开了用于特征点定位的方法和装置的各种实施例。 可以检测输入图像中的对象。 可以应用轮廓模型来确定检测到的对象的每个对象分量的特征点位置。 应用轮廓模型可以包括全局优化每个对象分量的特征点以找到全局能量最小值。 可以应用基于组件的形状模型来更新每个对象组件的各个特征点位置。
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公开(公告)号：US20140098988A1
公开(公告)日：2014-04-10
申请号：US13645453
申请日：2012-10-04
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Jonathan W. Brandt , Zhe Lin , Lubomir D. Bourdev , Vuong Le
CPC classification number: G06K9/00281 , G06K9/6209
Abstract: Various embodiments of methods and apparatus for feature point localization are disclosed. An object in an input image may be detected. A profile model may be applied to determine feature point locations for each object component of the detected object. Applying the profile model may include globally optimizing the feature points for each object component to find a global energy minimum. A component-based shape model may be applied to update the respective feature point locations for each object component.
Abstract translation: 公开了用于特征点定位的方法和装置的各种实施例。 可以检测输入图像中的对象。 可以应用轮廓模型来确定检测到的对象的每个对象分量的特征点位置。 应用轮廓模型可以包括全局优化每个对象分量的特征点以找到全局能量最小值。 可以应用基于组件的形状模型来更新每个对象组件的各个特征点位置。
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