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31. 发明授权

US08971645B1 Video categorization using heterogeneous signals 有权
标题翻译：使用异构信号的视频分类
公开(公告)号：US08971645B1
公开(公告)日：2015-03-03
申请号：US13475042
申请日：2012-05-18
申请人： Huazhong Ning , Zhen Li , Hrishikesh Aradhye
发明人： Huazhong Ning , Zhen Li , Hrishikesh Aradhye
IPC分类号： G06K9/00
CPC分类号： G06K9/6296 , G06K9/00718
摘要： A system, apparatus, and method for video categorization using heterogeneous signals are disclosed. One aspect of the disclosed embodiments is a method for categorizing a plurality of video streams. The method includes determining a plurality of features of at least some of the plurality of video streams, determining a category of a first video stream of the plurality of video stream based on its plurality of features, identifying, using a processor, a relationship between the first video stream and a second video stream of the plurality of videos, the relationship having an associated weight, and updating, using the processor, the category of the first video stream based on a first message determined using the associated weight.
摘要翻译：公开了一种使用异构信号进行视频分类的系统，装置和方法。所公开的实施例的一个方面是用于对多个视频流进行分类的方法。该方法包括确定多个视频流中的至少一些视频流的多个特征，基于其多个特征来确定多个视频流中的第一视频流的类别，使用处理器识别所述多个视频流之间的关系第一视频流和多个视频的第二视频流，所述关系具有相关联的权重，并且基于使用相关权重确定的第一消息，使用处理器更新第一视频流的类别。

32. 发明授权

US08948499B1 Method for online learning and recognition of visual behaviors 有权
标题翻译：在线学习和识别视觉行为的方法
公开(公告)号：US08948499B1
公开(公告)日：2015-02-03
申请号：US12962548
申请日：2010-12-07
申请人： Swarup Medasani , David L. Allen , Suhas E. Chelian , Yuri Owechko
发明人： Swarup Medasani , David L. Allen , Suhas E. Chelian , Yuri Owechko
IPC分类号： G06K9/62
CPC分类号： G06K9/469 , G06K9/00771 , G06K9/00785 , G06K9/6296
摘要： Described is a system for object and behavior recognition which utilizes a collection of modules which, when integrated, can automatically recognize, learn, and adapt to simple and complex visual behaviors. An object recognition module utilizes a cooperative swarm algorithm to classify an object in a domain. A graph-based object representation module is configured to use a graphical model to represent a spatial organization of the object within the domain. Additionally, a reasoning and recognition engine module consists of two sub-modules: a knowledge sub-module and a behavior recognition sub-module. The knowledge sub-module utilizes a Bayesian network, while the behavior recognition sub-module consists of layers of adaptive resonance theory clustering networks and a layer of a sustained temporal order recurrent temporal order network. The described invention has applications in video forensics, data mining, and intelligent video archiving.
摘要翻译：描述了一种用于对象和行为识别的系统，其利用模块集合，当集成时，可以自动识别，学习和适应简单和复杂的视觉行为。对象识别模块利用协作群算法对域中的对象进行分类。基于图形的对象表示模块被配置为使用图形模型来表示域内对象的空间组织。另外，推理和识别引擎模块由两个子模块组成：知识子模块和行为识别子模块。知识子模块利用贝叶斯网络，而行为识别子模块由自适应共振理论聚类网络层和持续时间顺序复现时间顺序网络层组成。所描述的发明在视频取证，数据挖掘和智能视频归档中具有应用。

33. 发明申请

US20140321704A1 METHOD, SYSTEM AND APPARATUS FOR TRACKING OBJECTS OF A SCENE 有权
标题翻译：用于跟踪场景对象的方法，系统和设备
公开(公告)号：US20140321704A1
公开(公告)日：2014-10-30
申请号：US14264668
申请日：2014-04-29
申请人： CANON KABUSHIKI KAISHA
发明人： Ashley John Partis
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06K9/00771 , G06K9/00778 , G06K9/6296 , G06K2009/3291 , G06T7/20 , G06T2207/20072 , G06T2207/30196 , G06T2207/30241
摘要： A method of tracking objects of a scene is disclosed. The method determines two or more tracks which have merged. Each track is associated with at least one of the objects and having a corresponding graph structure. Each graph structure comprising at least one node representing the corresponding track. A new node representing the merged tracks is created. The graph structures are added as children nodes of the new node to create a merged graph structure. A split between the objects associated with one of the tracks represented by the nodes of the merged graph structure is determined. Similarity between one or more of the nodes in the merged graph structure and foreground areas corresponding to split objects is determined. One of the nodes in the merged graph structure is selected based on the determined similarity. A new graph structure for tracking the objects is created, the new graph structure having the selected node at the root of the new graph structure.
摘要翻译：公开了一种跟踪场景对象的方法。该方法确定已合并的两个或多个轨道。每个轨道与至少一个对象相关联并且具有相应的图形结构。每个图形结构包括表示相应轨道的至少一个节点。创建表示合并轨道的新节点。图结构作为新节点的子节点添加，以创建一个合并的图形结构。确定与由合并图结构的节点表示的轨道之一相关联的对象之间的分割。确定合并图形结构中的一个或多个节点与对应于分割对象的前景区域之间的相似度。基于所确定的相似度来选择合并图结构中的一个节点。创建用于跟踪对象的新图形结构，新图形结构具有选定节点在新图形结构的根部。

34. 发明授权

US08792972B2 Real-time methods and systems for mapping a target region in the brain during surgery 有权
标题翻译：用于在手术期间绘制脑中目标区域的实时方法和系统
公开(公告)号：US08792972B2
公开(公告)日：2014-07-29
申请号：US12658351
申请日：2010-02-05
申请人： Adam Zaidel , Hagai Bergman , Zvi Israel
发明人： Adam Zaidel , Hagai Bergman , Zvi Israel
IPC分类号： A61B5/04
CPC分类号： A61N1/0529 , A61B5/04001 , A61B5/4082 , A61B5/7264 , A61B5/7267 , A61N1/0534 , A61N1/36082 , G06K9/6296 , G06K2209/057
摘要： A real-time method and system to accurately demarcate sub-territories of the subthalamic nucleus area in the brain during surgery, based on microelectrode recordings and a Hidden Markov Model. Root mean square and power spectral density of the microelectrode recordings are used to train and test Hidden Markov Model in identifying the dorsolateral oscillatory region and non-oscillatory sub-territories within the subthalamic nucleus. After the dorsolateral oscillatory region in the subthalamic nucleus is mapped, the microelectrodes are removed, and a macroelectrode is inserted in the mapped dorsolateral oscillatory region for producing deep brain simulation for treatment of Parkinson's disease.
摘要翻译：基于微电极记录和隐马尔科夫模型，在手术过程中，准确地划分脑内丘脑下丘脑区域的实时方法和系统。微电极记录的均方根和功率谱密度用于训练和测试隐马尔科夫模型，以识别下丘脑核内的背侧振荡区域和非振荡子区域。在下丘脑核中的背侧振荡区域被映射后，去除微电极，并且将大电极插入到映射的背外侧振荡区域中，以产生用于治疗帕金森病的深部脑模拟。

35. 发明授权

US08712096B2 Method and apparatus for detecting and tracking vehicles 有权
标题翻译：用于车辆检测和跟踪的方法和装置
公开(公告)号：US08712096B2
公开(公告)日：2014-04-29
申请号：US13041092
申请日：2011-03-04
申请人： Jiangjian Xiao , Harpreet Singh Sawhney , Hui Cheng
发明人： Jiangjian Xiao , Harpreet Singh Sawhney , Hui Cheng
IPC分类号： G06K9/00
CPC分类号： G06K9/6296 , G06K9/00624 , G06T7/254
摘要： The present invention relates to a method and apparatus for detecting and tracking vehicles. One embodiment of a system for detecting and tracking an object (e.g., vehicle) in a field of view includes a moving object indication stage for detecting a candidate object in a series of input video frames depicting the field of view and a track association stage that uses a joint probabilistic graph matching framework to associate an existing track with the candidate object.
摘要翻译：本发明涉及车辆检测跟踪方法和装置。用于在视野中检测和跟踪对象（例如，车辆）的系统的一个实施例包括用于检测描绘视场的一系列输入视频帧中的候选对象的移动对象指示级和跟踪关联级，使用联合概率图匹配框架将现有轨道与候选对象相关联。

36. 发明授权

US08560279B2 Method of determining the influence of a variable in a phenomenon 有权
标题翻译：确定变量在现象中的影响的方法
公开(公告)号：US08560279B2
公开(公告)日：2013-10-15
申请号：US13023181
申请日：2011-02-08
申请人： Robert Edward Callan
发明人： Robert Edward Callan
IPC分类号： G06F17/50
CPC分类号： G06F17/18 , G06F17/5009 , G06K9/00536 , G06K9/6296
摘要： A method of determining the influence of a variable in a phenomenon includes extracting a selected variable for analysis and conducting a sequence of graphical operations that includes other variables in the phenomenon. Calculating a variable influence indicator for the selected variable and repeating the steps for other selected variables enables an evaluation among the selected variables to determine their influence in the phenomenon.
摘要翻译：确定变量在现象中的影响的方法包括提取用于分析的选定变量并进行包括该现象中的其他变量的图形操作序列。计算所选变量的变量影响指标并重复其他选定变量的步骤可以使所选变量之间的评估确定其对该现象的影响。

37. 发明授权

US08457409B2 Cortex-like learning machine for temporal and hierarchical pattern recognition 失效
标题翻译： Cortex-like学习机，用于时间和层次模式识别
公开(公告)号：US08457409B2
公开(公告)日：2013-06-04
申请号：US12471341
申请日：2009-05-22
申请人： James Ting-Ho Lo
发明人： James Ting-Ho Lo
IPC分类号： G06K9/46 , G06K9/62 , G06E1/00
CPC分类号： G06K9/6232 , G06K9/6203 , G06K9/6296 , G06N3/02
摘要： A cortex-like learning machine, called a probabilistic associative memory (PAM), is disclosed for recognizing spatial and temporal patterns. A PAM is usually a multilayer or recurrent network of processing units (PUs). Each PU expands subvectors of a feature vector input to the PU into orthogonal vectors, and generates a probability distribution of the label of said feature vector, using expansion correlation matrices, which can be adjusted in supervised or unsupervised learning by a Hebbian-type rule. The PU also converts the probability distribution into a ternary vector to be included in feature subvectors that are input to PUs in the same or other layers. A masking matrix in each PU eliminates effect of corrupted components in query feature subvectors and enables maximal generalization by said PU and thereby that by the PAM. PAMs with proper learning can recognize rotated, translated and scaled patterns and are functional models of the cortex.
摘要翻译：公开了一种称为概率关联记忆（PAM）的皮质样学习机，用于识别空间和时间模式。 PAM通常是处理单元（PU）的多层或复现网络。每个PU将输入到PU的特征向量的子向量扩展成正交向量，并且使用扩展相关矩阵生成所述特征向量的标签的概率分布，所述扩展相关矩阵可以通过Hebbian类型规则在受监督或无监督学习中进行调整。 PU还将概率分布转换为三元向量，以包含在输入到相同层或其他层中的PU的特征子向量中。每个PU中的掩蔽矩阵消除了查询特征子向量中损坏的分量的影响，并且使得所述PU最大化泛化，从而使得由PAM最大化。具有适当学习的PAM可以识别旋转，翻译和缩放的模式，并且是皮质的功能模型。

38. 发明申请

US20130096878A1 STATE INFERENCE IN A HETEROGENEOUS SYSTEM 审中-公开
标题翻译：异质系统中的状态干扰
公开(公告)号：US20130096878A1
公开(公告)日：2013-04-18
申请号：US13806679
申请日：2010-06-24
申请人： Jyrki Lotjonen , Juha Koikkalainen , Jussi Mattila
发明人： Jyrki Lotjonen , Juha Koikkalainen , Jussi Mattila
IPC分类号： G06F17/00 , G06F17/18
CPC分类号： G06F17/00 , G06F7/24 , G06F16/9027 , G06F17/18 , G06K9/6253 , G06K9/6265 , G06K9/6296 , G06N7/005 , G16H50/20 , G16H50/30
摘要： The invention relates to inferring the state of a system of interest having a plurality of indicator values and possibly being heterogeneous in nature. A number of indicator values from a control state and from a comparison state are gathered. From these indicator values, classification power between the control and comparison states (measure of goodness) is computed. Difference values are computed for the indicator values from the system of interest based on the difference to the indicator values from control and comparison states. From a number of these indicators, composite indicators are formed, and composite measures of goodness and composite difference values are computed. A plurality of composite indicators may be formed at different levels. These indicators may be represented as a tree and grouped according to content, and at the same time they may be arranged according to the measure of goodness or some other value. The indicators, measures of goodness, and difference values may be visualized and shown to a user, who may use such a representation for inferring the state of the system.
摘要翻译：本发明涉及推断具有多个指标值并且本质上可能是异质的感兴趣系统的状态。收集来自控制状态和比较状态的多个指标值。根据这些指标值，计算控制和比较状态之间的分类能力（量度）。基于与控制和比较状态的指标值的差异，根据感兴趣系统的指标值计算差值。从多项指标中，形成综合指标，计算出良好和复合差值的综合指标。多个复合指示器可以形成在不同的水平。这些指标可以表示为一棵树，并根据内容进行分组，同时它们可以根据善意度量或某种其他价值进行排列。指标，善意度和差异值可以被可视化并显示给可以使用这种表示来推断系统状态的用户。

39. 发明授权

US08423490B2 Method for computer-aided learning of a neural network and neural network 失效
标题翻译：神经网络和神经网络的计算机辅助学习方法
公开(公告)号：US08423490B2
公开(公告)日：2013-04-16
申请号：US11992785
申请日：2006-09-20
申请人： Gustavo Deco , Martin Stetter , Miruna Szabo
发明人： Gustavo Deco , Martin Stetter , Miruna Szabo
IPC分类号： G06F15/18 , G06N3/08
CPC分类号： G06N3/08 , G06K9/6296
摘要： There is described a method for computer-aided learning of a neural network, with a plurality of neurons in which the neurons of the neural network are divided into at least two layers, comprising a first layer and a second layer crosslinked with the first layer. In the first layer input information is respectively represented by one or more characteristic values from one or several characteristics, wherein every characteristic value comprises one or more neurons of the first layer. A plurality of categories is stored in the second layer, wherein every category comprises one or more neurons of the second layer. For one or several pieces of input information, respectively at least one category in the second layer is assigned to the characteristic values of the input information in the first layer. Input information is entered into the first layer and subsequently at least one state variable of the neural network is determined and compared to the at least one category of this input information assigned in a preceding step. The crosslinking between the first and second layer is changed depending on the comparison result from a preceding step.
摘要翻译：描述了一种用于神经网络的计算机辅助学习的方法，其中神经网络的神经元被分成至少两层的多个神经元，其包括与第一层交联的第一层和第二层。在第一层中，输入信息分别由来自一个或几个特征的一个或多个特征值表示，其中每个特征值包括第一层的一个或多个神经元。多个类别被存储在第二层中，其中每个类别包括第二层的一个或多个神经元。对于一个或多个输入信息，第二层中的至少一个类别分配给第一层中的输入信息的特征值。输入信息被输入到第一层，随后确定神经网络的至少一个状态变量并将其与在前一步骤中分配的该输入信息的至少一个类别进行比较。第一层和第二层之间的交联根据前面步骤的比较结果而改变。

40. 发明授权

US08306329B2 System and method for searching handwritten texts 有权
标题翻译：用于搜索手写文本的系统和方法
公开(公告)号：US08306329B2
公开(公告)日：2012-11-06
申请号：US12618239
申请日：2009-11-13
申请人： Chiranjib Bhattacharyya , Sriraghavendra Ramaswamy
发明人： Chiranjib Bhattacharyya , Sriraghavendra Ramaswamy
IPC分类号： G06K9/00
CPC分类号： G06K9/00859 , G06K9/00422 , G06K9/6215 , G06K9/6296
摘要： A language-neutral method for searching online handwritten notes is provided. The different algorithms contained in this method enable querying online multilingual handwritten documents with substrings of words rather than just whole words. More particularly, two approaches are presented —one based on partial Fréchet distance calculations and the other based on a pair hidden Markov models. The partial Fréchet distance is adapted from the traditional Fréchet distance concept to match a subcurve or prefix of a query word. The pair hidden Markov model used in the present application is adapted from pair hidden Markov models used in bioinformatics as generative models of local and global alignment of biological sequences.
摘要翻译：提供了一种用于搜索在线手写笔记的语言中立的方法。该方法中包含的不同算法使得可以使用单词的子字符串查询多语种手写文档，而不仅仅是整个单词。更具体地，提出了两种方法 - 一种是基于部分Fréchet距离计算，另一种基于一对隐马尔可夫模型。部分Fréchet距离是根据传统的Fréchet距离概念进行调整，以匹配查询词的子曲线或前缀。本申请中使用的对隐马尔科夫模型是从生物信息学中使用的隐藏马尔科夫模型改编为生物序列的局部和全局比对的生成模型。

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