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31. 发明授权

US08469896B2 Method for segmenting a cardiovascular signal 有权
标题翻译：分割心血管信号的方法
公开(公告)号：US08469896B2
公开(公告)日：2013-06-25
申请号：US12308756
申请日：2006-06-26
申请人： Samuel Emil Schmidt , Claus Graff , Johannes Jan Struijk
发明人： Samuel Emil Schmidt , Claus Graff , Johannes Jan Struijk
IPC分类号： A61B5/02
CPC分类号： A61B7/04 , G06K9/0053 , G06K9/6296
摘要： This invention describes a method for dividing a substantial cycli cardiovascular signal into segments by determining the characteristics of said cyclic signal, wherein each cycle in said signal comprises at least two characteristic segments and wherein the method comprises the steps o identifying segments in a cycle based on prior knowledge of said segment characteristics and the step of verifying said identified segments based on a number of statistical parameters obtained from prior knowledge relating to said cyclic signal. Furthermore, the invention describes a system adapted to perform the above-described method.
摘要翻译：本发明描述了一种通过确定所述循环信号的特征将基本循环心血管信号划分为多个段的方法，其中所述信号中的每个周期包括至少两个特征段，并且其中该方法包括以下步骤：基于基于从与所述循环信号有关的现有知识获得的统计参数的数量来验证所述识别段的步骤。此外，本发明描述了适于执行上述方法的系统。

32. 发明授权

US08447710B1 Method and system for reducing links in a Bayesian network 有权
标题翻译：减少贝叶斯网络中链路的方法和系统
公开(公告)号：US08447710B1
公开(公告)日：2013-05-21
申请号：US12848485
申请日：2010-08-02
申请人： Sreerupa Das
发明人： Sreerupa Das
IPC分类号： G06F17/00
CPC分类号： G06N7/005 , G06K9/6296
摘要： A method and system for reducing a number of links in a Bayesian network. A first modified Bayesian network based on a primary Bayesian network that has a plurality of links is generated, wherein the first modified Bayesian network does not include a first subset of the plurality of links. A second modified Bayesian network based on the primary Bayesian network is generated, wherein the second modified Bayesian network does not include a second subset of the plurality of links. The first modified Bayesian network and the second modified Bayesian network are validated using a first data set to generate a first validation result and a second validation result. The primary Bayesian network is substituted with the first modified Bayesian network or the second modified Bayesian network based on the first validation result and the second validation result.
摘要翻译：一种用于减少贝叶斯网络中的链路数量的方法和系统。生成基于具有多个链路的主贝叶斯网络的第一修改贝叶斯网络，其中第一修改贝叶斯网络不包括多个链路的第一子集。生成基于主贝叶斯网络的第二修改贝叶斯网络，其中第二修改贝叶斯网络不包括多个链路的第二子集。使用第一数据集验证第一修改贝叶斯网络和第二修改贝叶斯网络以生成第一验证结果和第二验证结果。基于第一验证结果和第二验证结果，将主贝叶斯网络替换为第一修改贝叶斯网络或第二修改贝叶斯网络。

33. 发明授权

US08363959B2 Detecting irregularities 有权
标题翻译：检测不规范
公开(公告)号：US08363959B2
公开(公告)日：2013-01-29
申请号：US11909169
申请日：2006-03-21
申请人： Oren Boiman , Michal Irani
发明人： Oren Boiman , Michal Irani
IPC分类号： G06K9/00 , G06K9/68
CPC分类号： G06K9/6284 , G06K9/00342 , G06K9/6296 , G06T7/0004 , G06T7/97 , G06T2207/20016
摘要： Method and apparatus for inferring irregularities in query data relative to referential data includes attempting to compose the query data, like a puzzle, from large chunks of the referential data, and inferring irregularities in the query data based on at least the size of the matching chunks. The larger the size of a matching chunk, the more likely it is that its corresponding region in the query data is valid and not irregular. Regions in the query data which cannot be composed from the referential data, or can only be composed using small fragmented pieces and not large chunks of the referential data, are considered irregular. The method and apparatus is applicable to all types of signals, including images, video data, medical data, one-dimensional signals and multi-dimensional signals, and can be used to identify, inter alia, suspicious behaviors, suspicious objects, irregular patterns and defects in goods.
摘要翻译：用于推断查询数据中相对于参照数据的不规则性的方法和装置包括尝试从参考数据的大块组合查询数据，如拼图，以及至少基于匹配块的大小来推断查询数据中的不规则性。匹配块的大小越大，查询数据中对应的区域越有可能是不正规的。不能由参考数据组成的查询数据中的区域，或只能使用小碎片而不是大块参考数据组成的区域被认为是不规则的。该方法和装置适用于所有类型的信号，包括图像，视频数据，医疗数据，一维信号和多维信号，并且可用于识别可疑行为，可疑对象，不规则图案和商品缺陷

34. 发明授权

US08363950B2 Combining online and offline recognizers in a handwriting recognition system 有权
公开(公告)号：US08363950B2
公开(公告)日：2013-01-29
申请号：US13426427
申请日：2012-03-21
申请人： Xinjian Chen , Dongmei Zhang , Yu Zou , Ming Chang , Shi Han , Jian Wang
发明人： Xinjian Chen , Dongmei Zhang , Yu Zou , Ming Chang , Shi Han , Jian Wang
IPC分类号： G06K9/00 , G06F17/00
CPC分类号： G06K9/00973 , G06K9/6292 , G06K9/6296
摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

35. 发明授权

US08300942B2 Area extraction program, character recognition program, and character recognition device 有权
标题翻译：区域提取程序，字符识别程序和字符识别装置
公开(公告)号：US08300942B2
公开(公告)日：2012-10-30
申请号：US12366004
申请日：2009-02-05
申请人： Hiroaki Takebe , Katsuhito Fujimoto
发明人： Hiroaki Takebe , Katsuhito Fujimoto
IPC分类号： G06K9/46 , G06K9/72
CPC分类号： G06K9/72 , G06K9/348 , G06K9/6296 , G06K2209/01
摘要： An area extraction method including obtaining a character lattice showing a connection relation between unit areas, which are obtained by separating a character string pattern in an image into patterns each recognized as corresponding to a single character, judging whether or not all combinations of each of the unit areas in the obtained character lattice and each of the unit areas in a regular lattice defining a regular connection relation between the unit areas are likely to be established, generating a path coupling between nodes corresponding to the combination of the unit areas which is determined as likely to be established, determining an optimum path from the generated paths based on a degree of coincidence with the regular lattice or the character lattice, and extracting from an image the unit areas in the character lattice corresponding to the determined optimum path.
摘要翻译：一种区域提取方法，其包括获得通过将图像中的字符串图案分离成各自识别为与单个字符相对应的图案而获得的单元区域之间的连接关系的字符格子，判断是否将每个获得的字符格中的单位区域和规定单位区域之间的规则连接关系的规则格子中的每个单位区域很可能被建立，生成对应于单位区域的组合的节点之间的路径耦合，单元区域被确定为可能建立起来，基于与规则格子或字符格子的一致程度从所生成的路径确定最佳路径，以及从图像中提取与所确定的最佳路径对应的字符格点中的单位区域。

36. 发明授权

US08271414B2 Network characterization, feature extraction and application to classification 有权
标题翻译：网络表征，特征提取和应用分类
公开(公告)号：US08271414B2
公开(公告)日：2012-09-18
申请号：US12508791
申请日：2009-07-24
申请人： Guillermo Alberto Cecchi , Srinivas Ravi Viraraghava Iyengar , Avi Ma'ayan , Ravishankar Rao , Gustavo Alejandro Stolovitzky , John Michael Wagner
发明人： Guillermo Alberto Cecchi , Srinivas Ravi Viraraghava Iyengar , Avi Ma'ayan , Ravishankar Rao , Gustavo Alejandro Stolovitzky , John Michael Wagner
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G06K9/6296 , G06F19/00 , G06N99/005 , G16H50/70
摘要： Methods, systems and apparatus for characterizing networks are presented. For example, a method of characterizing a network represented by a plurality of nodes and a plurality of edges is provided. The method may be implemented on a processor device and includes calculating, for example, by the processor device, a passthrough count of at least a portion of the network. The passthrough count includes a count of a number of passthroughs in the at least a portion of the network. A passthrough includes one of the plurality of nodes, a directed edge of the plurality of edges coupled to the one of the plurality of nodes, and another edge of the plurality of edges coupled to the one of the plurality of nodes. At most one of the directed edge and the other edge is directed towards the one of the plurality of nodes. At most one of the directed edge and the other edge is directed away from the one of the plurality of nodes.
摘要翻译：提出了表征网络的方法，系统和装置。例如，提供了表征由多个节点和多个边缘表示的网络的方法。该方法可以在处理器设备上实现，并且包括例如由处理器设备计算网络的至少一部分的直通计数。通过计数包括在网络的至少一部分中的通过数量的计数。传播包括多个节点中的一个，耦合到多个节点中的一个节点的多个边缘中的有向边缘，以及耦合到多个节点中的一个节点的多个边缘中的另一个边缘。有向边缘和另一边缘中的至少一个被引导到多个节点中的一个。定向边缘和另一边缘中的至少一个被引导离开多个节点中的一个。

37. 发明授权

US08149278B2 System and method for modeling movement of objects using probabilistic graphs obtained from surveillance data 有权
标题翻译：使用从监视数据获得的概率图来建模物体运动的系统和方法
公开(公告)号：US08149278B2
公开(公告)日：2012-04-03
申请号：US11671016
申请日：2007-02-05
申请人： Christopher R. Wren , Yuri A. Ivanov , Alexander Sorokin , Ishwinder Kaur Banga
发明人： Christopher R. Wren , Yuri A. Ivanov , Alexander Sorokin , Ishwinder Kaur Banga
IPC分类号： H04N7/18
CPC分类号： H04N7/18 , G06K9/00771 , G06K9/6296
摘要： A method and system tracks models and predict object movement in an environment. Sequences of temporally and spatially adjacent events sensed by the set of sensors are linked to form a set of tracklets. Each tracklet has an associated starting and terminating location. The tracklets are used to construct a directed graph including starting nodes, terminating nodes, and, intermediate nodes connected by edges. The intermediate nodes can be split nodes where tracklets diverge onto different tracks, and join nodes where multiple tracklets converge onto a single path. Probabilities are assigned to the edges to model and predict movement of the objects in the environment.
摘要翻译：方法和系统跟踪模型并预测环境中的物体移动。由传感器组感测的时间和空间相邻事件的序列被链接以形成一组轨迹。每个轨迹具有相关的起始和终止位置。轨迹用于构造有向图，包括起始节点，终止节点以及通过边连接的中间节点。中间节点可以是轨迹分散到不同轨道上的分割节点，并且连接多个轨迹收敛到单个路径上的节点。将概率分配给边缘以建模和预测对象在环境中的移动。

38. 发明授权

US08121968B2 Long-term memory in a video analysis system 有权
标题翻译：视频分析系统的长期记忆
公开(公告)号：US08121968B2
公开(公告)日：2012-02-21
申请号：US12208551
申请日：2008-09-11
申请人： John Eric Eaton , Wesley Kenneth Cobb , Ming-Jung Seow , David Samuel Friedlander , Gang Xu
发明人： John Eric Eaton , Wesley Kenneth Cobb , Ming-Jung Seow , David Samuel Friedlander , Gang Xu
IPC分类号： G06F17/00 , G06F15/18 , G06N5/02 , G06K9/62 , G06K9/46 , G06K9/66
CPC分类号： G06N99/005 , G06K9/00335 , G06K9/00771 , G06K9/6296
摘要： A long-term memory used to store and retrieve information learned while a video analysis system observes a stream of video frames is disclosed. The long-term memory provides a memory with a capacity that grows in size gracefully, as events are observed over time. Additionally, the long-term memory may encode events, represented by sub-graphs of a neural network. Further, rather than predefining a number of patterns recognized and manipulated by the long-term memory, embodiments of the invention provide a long-term memory where the size of a feature dimension (used to determine the similarity between different observed events) may grow dynamically as necessary, depending on the actual events observed in a sequence of video frames.
摘要翻译：公开了一种用于存储和检索在视频分析系统观察视频帧流时学习的信息的长期存储器。长期记忆提供了一个能力随着时间的推移而随着时间的推移而增长的记忆。另外，长期记忆可以编码由神经网络的子图表示的事件。此外，本发明的实施例不是预先确定由长期记忆识别和操纵的多个模式，而是提供长期存储器，其中特征维度（用于确定不同观察到的事件之间的相似性）的大小可以动态地增长根据需要，取决于视频帧序列中观察到的实际事件。

39. 发明授权

US08064688B2 Object recognizer and detector for two-dimensional images using Bayesian network based classifier 有权
标题翻译：使用基于贝叶斯网络的分类器的二维图像的对象识别器和检测器
公开(公告)号：US08064688B2
公开(公告)日：2011-11-22
申请号：US12259371
申请日：2008-10-28
申请人： Henry Schneiderman
发明人： Henry Schneiderman
IPC分类号： G06K9/62
CPC分类号： G06K9/6278 , G06K9/00228 , G06K9/00241 , G06K9/00288 , G06K9/6227 , G06K9/6282 , G06K9/6296 , G06K9/66
摘要： A system and method for determining a classifier to discriminate between two classes—object or non-object. The classifier may be used by an object detection program to detect presence of a 3D object in a 2D image (e.g., a photograph or an X-ray image). The overall classifier is constructed of a sequence of classifiers (or “sub-classifiers”), where each such classifier is based on a ratio of two graphical probability models (e.g., Bayesian networks). A discrete-valued variable representation at each node in a Bayesian network by a two-stage process of tree-structured vector quantization is discussed. The overall classifier may be part of an object detector program that is trained to automatically detect many different types of 3D objects (e.g., human faces, airplanes, cars, etc.). Computationally efficient statistical methods to evaluate overall classifiers are disclosed. The Bayesian network-based classifier may also be used to determine if two observations (e.g., two images) belong to the same category. For example, in case of face recognition, the classifier may determine whether two photographs are of the same person. A method to provide lighting correction or adjustment to compensate for differences in various lighting conditions of input images is disclosed as well. As per the rules governing abstracts, the content of this abstract should not be used to construe the claims in this application.
摘要翻译：一种用于确定分类器以区分两个类（对象或非对象）的系统和方法。分类器可以被对象检测程序用于检测2D图像中的3D对象的存在（例如，照片或X射线图像）。总体分类器由分类器（或“分类器”）的序列构成，其中每个这样的分类器基于两个图形概率模型（例如，贝叶斯网络）的比率。讨论了通过树结构矢量量化的两阶段过程在贝叶斯网络中的每个节点处的离散值变量表示。总体分类器可以是被训练来自动检测许多不同类型的3D对象（例如，人脸，飞机，汽车等）的物体检测器程序的一部分。公开了用于评估整体分类器的计算有效的统计方法。也可以使用基于贝叶斯网络的分类器来确定两个观察值（例如，两个图像）是否属于同一类别。例如，在脸部识别的情况下，分类器可以确定两张照片是否是同一人物。还公开了提供照明校正或调整以补偿输入图像的各种照明条件的差异的方法。根据管理摘要的规则，本摘要的内容不应用于解释本申请中的权利要求。

40. 发明授权

US08059864B2 System and method of image-based space detection 有权
标题翻译：基于图像的空间检测系统和方法
公开(公告)号：US08059864B2
公开(公告)日：2011-11-15
申请号：US12111190
申请日：2008-04-28
申请人： Ching-Chun Huang , Yao-Jen Chang , Ruei-Cheng Wu , Cheng-Peng Kuan
发明人： Ching-Chun Huang , Yao-Jen Chang , Ruei-Cheng Wu , Cheng-Peng Kuan
IPC分类号： G06K9/00
CPC分类号： G08G1/14 , G06K9/00785 , G06K9/6277 , G06K9/6296
摘要： Disclosed is a system and method of image-based space detection. The system includes an image selection module, a 3-layer detection mechanism and an optimization module. At least one image processing area that may affect space-status judgment is selected from plural image processing areas. The 3-layer detection mechanism having an observation layer, a labeling layer, and a semantic layer observes the information about the selected image processing area, associates with a local classification model, and adjacent local constraint model and a global semantics model to completely describe the probability distribution of the links among the three layers, and provide global label constraint information. The optimization module analyzes the probability distribution and global label constraint information, and generates an image-based optimized space detection result.
摘要翻译：公开了基于图像的空间检测的系统和方法。该系统包括图像选择模块，3层检测机构和优化模块。从多个图像处理区域中选择可能影响空间状态判断的至少一个图像处理区域。具有观察层，标记层和语义层的3层检测机构观察关于所选图像处理区域的信息，与本地分类模型相关联，以及相邻局部约束模型和全局语义模型，以完全描述在三层之间的链路的概率分布，并提供全局标签约束信息。优化模块分析概率分布和全局标签约束信息，并生成基于图像的优化空间检测结果。

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