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21. 发明申请

US20170011536A1 METHOD AND APPARATUS FOR EXTENDED PHASE CORRECTION IN PHASE SENSITIVE MAGNETIC RESONANCE IMAGING 审中-公开
标题翻译：相敏感磁共振成像中扩展相位校正的方法与装置
公开(公告)号：US20170011536A1
公开(公告)日：2017-01-12
申请号：US15270341
申请日：2016-09-20
申请人： Board of Regents, The University of Texas System
发明人： Jingfei MA
IPC分类号： G06T11/00 , G06K9/62 , G06T7/00 , G06K9/46 , G06T5/00 , G06T5/20
CPC分类号： G06T11/008 , A61B5/055 , G01R33/4828 , G01R33/5602 , G01R33/5607 , G01R33/565 , G06K9/46 , G06K9/4638 , G06K9/6215 , G06K9/623 , G06K9/6276 , G06T5/00 , G06T5/002 , G06T5/20 , G06T7/0012 , G06T2207/10088 , G06T2207/20021
摘要： Methods, apparatuses, systems, and software for extended phase correction in phase sensitive Magnetic Resonance Imaging. A magnetic resonance image or images may be loaded into a memory. Two vector images A and B associated with the loaded image or images may be calculated either explicitly or implicitly so that a vector orientation by one of the two vector images at a pixel is substantially determined by a background or error phase at the pixel, and the vector orientation at the pixel by the other vector image is substantially different from that determined by the background or error phase at the pixel. A sequenced region growing phase correction algorithm may be applied to the vector images A and B to construct a new vector image V so that a vector orientation of V at each pixel is substantially determined by the background or error phase at the pixel. A phase corrected magnetic resonance image or images may be generated using the vector image V, and the phase corrected magnetic resonance image or images may be displayed or archived.
摘要翻译：用于相位敏感磁共振成像的扩展相位校正的方法，装置，系统和软件。磁共振图像或图像可以被加载到存储器中。与加载的图像或图像相关联的两个矢量图像A和B可以被明确地或隐含地计算，使得像素处的两个矢量图像之一的矢量取向基本上由像素处的背景或误差相位确定，并且通过其他矢量图像在像素处的矢量取向与由像素处的背景或误差相位确定的矢量方向实质上不同。顺序区域生长相位校正算法可以应用于矢量图像A和B以构造新的矢量图像V，使得每个像素处的V的矢量取向基本上由像素处的背景或误差相位确定。可以使用向量图像V生成相位校正的磁共振图像或图像，并且可以显示或归档相位校正的磁共振图像或图像。

22. 发明申请

US20170004387A1 METHOD AND SYSTEM FOR RECOGNIZING FACES 有权
标题翻译：识别方法和系统
公开(公告)号：US20170004387A1
公开(公告)日：2017-01-05
申请号：US15100199
申请日：2013-11-30
申请人： BEIJING SENSETIME TECHNOLOGY DEVELOPMENT CO., LTD
发明人： Xiaoou Tang , Chaochao Lu , Deli Zhao
IPC分类号： G06K9/62 , G06K9/46 , G06K9/00
CPC分类号： G06K9/6276 , G06K9/00234 , G06K9/00281 , G06K9/00288 , G06K9/469 , G06K9/6215
摘要： A method and a system for recognizing faces have been disclosed. The method may comprise: retrieving a pair of face images; segmenting each of the retrieved face images into a plurality of image patches, wherein each patch in one image and a corresponding one in the other image form a pair of patches; determining a first similarity of each pair of patches; determining, from all pair of patches, a second similarity of the pair of face images; and fusing the first similarity determined for the each pair of patches and the second similarity determined for the pair of face images.
摘要翻译：已经公开了一种用于识别面部的方法和系统。该方法可以包括：检索一对面部图像; 将每个检索到的面部图像分割成多个图像斑块，其中一个图像中的每个贴片和另一个图像中的相应图像形成一对贴片; 确定每对补丁的第一相似性; 从所述一对贴片确定所述一对脸部图像的第二相似度; 并且对于每对贴片确定的第一相似性和对于所述一对面部图像确定的第二相似性进行融合。

23. 发明申请

US20160282394A1 ELECTRONIC COMPONENT CLASSIFICATION 有权
标题翻译：电子元件分类
公开(公告)号：US20160282394A1
公开(公告)日：2016-09-29
申请号：US15178708
申请日：2016-06-10
申请人： Battelle Memorial Institute
发明人： Larry J. House , Dale C. Engelhart
IPC分类号： G01R29/26 , G06F21/44
CPC分类号： G01R29/26 , G06F21/44 , G06K9/00536 , G06K9/00577 , G06K9/6276
摘要： A system and method of electronic component authentication or component classification can reduce the vulnerability of systems (e.g., satellites, weapons, critical infrastructure, aerospace, automotive, medical systems) to counterfeits. Intrinsic deterministically random property data can be obtained from a set of authentic electronic components, processed, and clustered to create a classifier that can distinguish whether an unknown electronic component is authentic or counterfeit.
摘要翻译：电子部件认证或组件分类的系统和方法可以将系统（例如，卫星，武器，关键基础设施，航空航天，汽车，医疗系统）的脆弱性降低到假冒。可以从一组可靠的电子组件获得固有的确定性随机属性数据，进行处理和聚类，以创建可以区分未知电子组件是真实还是假冒的分类器。

24. 发明授权

US09406030B2 System and methods for computerized machine-learning based authentication of electronic documents including use of linear programming for classification 有权
标题翻译：基于计算机机器学习的电子文件认证的系统和方法，包括使用线性规划进行分类
公开(公告)号：US09406030B2
公开(公告)日：2016-08-02
申请号：US14235658
申请日：2012-07-23
申请人： Guy Dolev , Sergey Markin , Avi Bar-Nissim , Asher Uziel
发明人： Guy Dolev , Sergey Markin , Avi Bar-Nissim , Asher Uziel
IPC分类号： G06F15/18 , G06N99/00 , G06K9/00 , G06K9/62
CPC分类号： G06N99/005 , G06K9/00442 , G06K9/6276
摘要： Electronic document classification comprising providing training documents sorted into classes; linear programming including selecting inputs which maximize an output, given constraints on inputs, the output maximized being a difference between: a. first estimated probability that a document instance will be correctly classified, by a classifier corresponding to given inputs, as belonging to its own class, and b. second estimated probability that document instance will be classified, by the classifier, as not belonging to its own class; and classifying electronic document instances into classes, using a preferred classifier corresponding, to the inputs selected by the linear programming. A computerized electronic document forgery detection method provides training documents and uses a processor to select value-ranges of non-trivial parameters, such that selected values-range(s) of parameters are typical to an authentic document of given class, and atypical to a forged document of same class.
摘要翻译：电子文件分类包括提供分类到课堂的训练文件; 线性规划包括选择最大化输出的输入，给定输入约束，最大化的输出是：a。第一个概率是通过对应给定输入的分类器将文档实例正确分类为属于其自己的类，以及b。文件实例将被分类器分类为属于自己的类的第二个估计概率; 并且将电子文档实例分类到类中，使用对应于由线性规划选择的输入的优选分类器。计算机化的电子文件伪造检测方法提供训练文件并且使用处理器来选择非平凡参数的值范围，使得所选择的参数范围对于给定类的真实文档是典型的，并且非典型地伪造同类文件。

25. 发明申请

US20160189000A1 SCALABLE IMAGE MATCHING 审中-公开
标题翻译：可图像匹配
公开(公告)号：US20160189000A1
公开(公告)日：2016-06-30
申请号：US15063050
申请日：2016-03-07
申请人： A9.com, Inc.
发明人： SIMANT DUBE , SUNIL RAMESH , XIAOFAN LIN , ARNAB SANAT KUMAR DHUA , COLIN JON TAYLOR , JAISHANKER K. PILLAI
IPC分类号： G06K9/62
CPC分类号： G06K9/6215 , G06F17/30247 , G06K9/00523 , G06K9/4676 , G06K9/52 , G06K9/6206 , G06K9/6211 , G06K9/6218 , G06K9/6232 , G06K9/6256 , G06K9/6267 , G06K9/6269 , G06K9/6276 , G06K9/6277 , G06K9/6284 , G06K9/66 , G06K2209/19 , H04N19/426 , H04N19/90
摘要： Various embodiments may increase scalability of image representations stored in a database for use in image matching and retrieval. For example, a system providing image matching can obtain images of a number of inventory items, extract features from each image using a feature extraction algorithm, and transform the same into their feature descriptor representations. These feature descriptor representations can be subsequently stored and used to compare against query images submitted by users. Though the size of each feature descriptor representation isn't particularly large, the total number of these descriptors requires a substantial amount of storage space. Accordingly, feature descriptor representations are compressed to minimize storage and, in one example, machine learning can be used to compensate for information lost as a result of the compression.
摘要翻译：各种实施例可以增加存储在用于图像匹配和检索的数据库中的图像表示的可扩展性。例如，提供图像匹配的系统可以获得多个库存物品的图像，使用特征提取算法从每个图像中提取特征，并将其转换成它们的特征描述符表示。这些特征描述符表示可随后存储并用于与用户提交的查询图像进行比较。虽然每个特征描述符表示的大小不是特别大，但是这些描述符的总数需要大量的存储空间。因此，压缩特征描述符表示以最小化存储，并且在一个示例中，可以使用机器学习来补偿由于压缩而丢失的信息。

26. 发明申请

US20160163522A1 Early detection of hepatocellular carcinoma in high risk populations using MALDI-TOF Mass Spectrometry 审中-公开
标题翻译：使用MALDI-TOF质谱法在高危人群中早期检测肝细胞癌
公开(公告)号：US20160163522A1
公开(公告)日：2016-06-09
申请号：US14936847
申请日：2015-11-10
申请人： Biodesix, Inc.
发明人： Joanna Röder , Carlos Oliveira , Julia Grigorieva , Heinrich Röder , Devalingam Mahalingam
IPC分类号： H01J49/00 , H01J49/16
CPC分类号： H01J49/0036 , G01N33/57438 , G01N33/6851 , G06F19/00 , G06K9/00147 , G06K9/00536 , G06K9/6228 , G06K9/6276 , G16H10/40 , G16H50/70 , H01J49/164 , Y02A90/26
摘要： Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.
摘要翻译：在肝病患者中检测到肝细胞癌（HCC）。将来自患者的血液样品的质谱数据与来自多个其他肝病患者的质谱数据进行比较，所述其他患者包括具有和不具有HCC的患者，在配置为分类器的通用计算机中。分类器为测试样本生成类标签，如HCC或No HCC。还公开了一种用于肝病患者中HCC早期检测的实验室系统。还描述了使用AFP测量和来自肺癌患者血液样品的类别标记质谱数据形式的分类参考文献的替代测试策略，包括多阶段测试。

27. 发明申请

US20160125232A1 DYNAMIC FACE IDENTIFICATION 有权
标题翻译：动态脸部识别
公开(公告)号：US20160125232A1
公开(公告)日：2016-05-05
申请号：US14532214
申请日：2014-11-04
申请人： Hewlett-Packard Development Company, L.P.
发明人： Tong Zhang
IPC分类号： G06K9/00 , H04N5/77 , G06T7/20 , G06K9/62 , G06K9/48
CPC分类号： G06T7/251 , G06K9/00288 , G06K9/00771 , G06K9/6222 , G06K9/6276 , H04N5/77
摘要： Systems and methods associated with dynamic face identification are disclosed. One example method includes matching a query face against a set of clusters in a dynamic collection. Matching the query face against the set of clusters may facilitate identifying a person associated with the query face. The example method also includes matching the query face against a set of images in a static gallery to identify the person. Matching the query face against the static gallery may be performed when matching the query face against the set of clusters fails to identify the person. The example method also includes updating the set of clusters in the dynamic collection using the query face.
摘要翻译：公开了与动态人脸识别相关的系统和方法。一个示例性方法包括将查询面与动态集合中的一组集群进行匹配。将查询面对与集合集匹配可以有助于识别与查询面相关联的人。该示例方法还包括将查询面与在静态画廊中的一组图像相匹配以识别该人。当查询面对与集合的集合匹配不能识别人时，可以执行将查询面与静态画廊匹配。示例方法还包括使用查询面更新动态集合中的集合集。

28. 发明授权

US09298172B2 Method and apparatus for improved reward-based learning using adaptive distance metrics 有权
标题翻译：使用自适应距离度量改进基于奖励的学习的方法和装置
公开(公告)号：US09298172B2
公开(公告)日：2016-03-29
申请号：US11870661
申请日：2007-10-11
申请人： Gerald J. Tesauro , Kilian Q. Weinberger
发明人： Gerald J. Tesauro , Kilian Q. Weinberger
IPC分类号： G06F15/18 , G05B13/02 , G06K9/62 , G06N5/02
CPC分类号： G05B13/0265 , G06K9/6267 , G06K9/6276 , G06N5/02 , G06N99/005
摘要： The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance metric and a distance-based function approximator estimating long-range expected value are then initialized, where the distance metric computes a distance between two (state, action) pairs, and the distance metric and function approximator are adjusted such that a Bellman error measure of the function approximator on the set of exemplars is minimized. A management policy is then derived based on the trained distance metric and function approximator.
摘要翻译：本发明是用于管理或控制系统或工厂的策略的奖励学习的方法和装置。在一个实施例中，用于基于奖励的学习的方法包括接收一组一个或多个示例，其中至少两个示例包括用于系统的（状态，动作）对，并且所述示例中的至少一个包括立即响应（状态，动作）对的奖励。然后初始化距离度量和基于距离的函数逼近器估计长距离期望值，其中距离度量计算两个（状态，动作）对之间的距离，并且调整距离度量和函数近似器使得Bellman误差功能近似器对该示例组的测量被最小化。然后基于经过训练的距离度量和函数近似器导出管理策略。

29. 发明授权

US09286540B2 Fast dense patch search and quantization 有权
标题翻译：快速密集补丁搜索和量化
公开(公告)号：US09286540B2
公开(公告)日：2016-03-15
申请号：US14085488
申请日：2013-11-20
申请人： Adobe Systems Incorporated
发明人： Zhe Lin , Jianchao Yang , Hailin Jin , Xin Lu
IPC分类号： G06K9/68 , G06K9/46 , G06K9/62
CPC分类号： G06K9/4642 , G06K9/6273 , G06K9/6276
摘要： In techniques for fast dense patch search and quantization, partition center patches are determined for partitions of example image patches. Patch groups of an image each include similar image patches and a reference image patch that represents a respective patch group. A partition center patch of the partitions is determined as a nearest neighbor to the reference image patch of a patch group. The partition center patch can be determined based on a single-nearest neighbor (1-NN) distance determination, and the determined partition center patch is allocated as the nearest neighbor to the similar image patches in the patch group. Alternatively, a group of nearby partition center patches are determined as the nearest neighbors to the reference image patch based on a k-nearest neighbor (k-NN) distance determination, and the nearest neighbor to each of the similar image patches in the patch group is determined from the nearby partition center patches.
摘要翻译：在快速密集补丁搜索和量化的技术中，为示例图像补丁的分区确定分区中心补丁。图像的补丁组各自包括相似的图像补丁和代表相应补丁组的参考图像补丁。分区的分区中心补丁被确定为补丁组的参考图像补丁的最近邻。可以基于单个最近邻居（1-NN）距离确定来确定分区中心补丁，并且将所确定的分区中心补丁分配为补丁组中的相似图像补丁的最近邻。或者，基于k个最近邻（k-NN）距离确定，将一组附近的分区中心补丁确定为参考图像补丁的最近邻，并且补丁组中每个相似图像补丁的最近邻是从附近的分区中心补丁确定的。

30. 发明申请

US20150379708A1 METHODS AND SYSTEMS FOR VESSEL BIFURCATION DETECTION 审中-公开
标题翻译：用于船舶分岔检测的方法和系统
公开(公告)号：US20150379708A1
公开(公告)日：2015-12-31
申请号：US14764926
申请日：2014-01-31
申请人： UNIVERSITY OF IOWA RESEARCH FOUNDATION
发明人： Michael ABRAMOFF , Gwenole QUELLEC
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06K9/6247 , G06K9/6256 , G06K9/6276 , G06K2209/051 , G06T2207/10024 , G06T2207/20081 , G06T2207/30041 , G06T2207/30096 , G06T2207/30101
摘要： Provided are systems and methods for analyzing images. An exemplary method can comprise receiving at least one image having one or more annotations indicating a feature. The method can comprise generating training images from the at least one image. Each training image can be based on a respective section of the at least one image. The training images can comprise positive images having the feature and negative images without the feature. The method can comprise generating a feature space based on the positive images and the negative images. The method can further comprise identifying the feature in one or more unclassified images based upon the feature space.
摘要翻译：提供了用于分析图像的系统和方法。示例性方法可以包括接收具有指示特征的一个或多个注释的至少一个图像。该方法可以包括从至少一个图像生成训练图像。每个训练图像可以基于至少一个图像的相应部分。训练图像可以包括没有特征的具有特征和负图像的正像。该方法可以包括基于正图像和负图像生成特征空间。该方法还可以包括基于特征空间来识别一个或多个未分类图像中的特征。

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