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51. 发明申请

US20080112625A1 COLLECTIVE MEDIA ANNOTATION USING UNDIRECTED RANDOM FIELD MODELS 有权
标题翻译：使用接收的随机场模型的集合媒体注释
公开(公告)号：US20080112625A1
公开(公告)日：2008-05-15
申请号：US11558826
申请日：2006-11-10
申请人： Matthew L. Cooper
发明人： Matthew L. Cooper
IPC分类号： G06K9/62
CPC分类号： G06K9/6297
摘要： In an embodiment, the present invention relates to a method for semantic analysis of digital multimedia. In an embodiment of the invention, low level features are extracted representative of one or more concepts. A discriminative classifier is trained using these low level features. A collective annotation model is built based on the discriminative classifiers. In various embodiments of the invention, the frame work is totally generic and can be applied with any number of low-level features or discriminative classifiers. Further, the analysis makes no domain specific assumptions, and can be applied to activity analysis or other scenarios without modification. The framework admits the inclusion of a broad class of potential functions, hence enabling multi-modal analysis and the fusion of heterogeneous information sources.
摘要翻译：在一个实施例中，本发明涉及数字多媒体的语义分析方法。在本发明的实施例中，提取代表一个或多个概念的低级特征。使用这些低级特征来训练鉴别分类器。基于辨别分类器构建了集体注释模型。在本发明的各种实施例中，帧功能是完全通用的，并且可以应用任何数量的低级特征或辨别分类器。此外，该分析没有域特定的假设，并且可以不经修改地应用于活动分析或其他场景。该框架承认包含广泛的潜在功能，从而实现多模式分析和异构信息源的融合。

52. 发明申请

US20070260435A1 Abnormal behavior detection apparatus 审中-公开
标题翻译：异常行为检测装置
公开(公告)号：US20070260435A1
公开(公告)日：2007-11-08
申请号：US11822457
申请日：2007-07-06
申请人： Yuko Matsunaga , Kenji Yamanishi
发明人： Yuko Matsunaga , Kenji Yamanishi
IPC分类号： G06F17/10
CPC分类号： G06F17/18 , G06K9/6297
摘要： Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.
摘要翻译：提供一串向量数据作为输入数据，概率分布估计装置通过使用具有隐藏变量的随机模型来估计通过连续读取向量数据序列而发生每个数据的概率分布。具体地，概率分布估计装置读取具有用于输入数据的值的隐藏变量的随机模型的参数的值，通过使用随机模型计算输入数据发生的确定性，响应于新的读取数据与过去的数据被遗忘，并产生几个参数的值。通过使用从概率分布估计装置接收的参数值，异常检测单元计算作为异常行为程度的数据的信息量，以产生异常行为程度。

53. 发明申请

US20070260434A1 Apparatus and program for detecting abnormal behavior 审中-公开
标题翻译：用于检测异常行为的装置和程序
公开(公告)号：US20070260434A1
公开(公告)日：2007-11-08
申请号：US11822455
申请日：2007-07-06
申请人： Yuko Matsunaga , Kenji Yamanishi
发明人： Yuko Matsunaga , Kenji Yamanishi
IPC分类号： G06F17/10
CPC分类号： G06F17/18 , G06K9/6297
摘要： Supplied with a string of vector data as input data, a probabilistic distribution estimation apparatus estimates, by using a stochastic model having hidden variables, a probabilistic distribution in which each data occurs by successively reading the train of vector data. Specifically, the probabilistic distribution estimation apparatus reads values of parameters of the stochastic model having the hidden variables for a value of the input data, calculates, by using the stochastic model, a certainty in which the input data occurs, renews the parameters in response to new read data with past data forgotten, and produce several parameter's values. By using the parameter's values received from the probabilistic distribution estimation apparatus, an abnormality detection unit calculates an information amount of data as an abnormal behavior degree to produce the abnormal behavior degree.
摘要翻译：提供一串向量数据作为输入数据，概率分布估计装置通过使用具有隐藏变量的随机模型来估计通过连续读取向量数据序列而发生每个数据的概率分布。具体地，概率分布估计装置读取具有用于输入数据的值的隐藏变量的随机模型的参数的值，通过使用随机模型计算输入数据发生的确定性，响应于新的读取数据与过去的数据被遗忘，并产生几个参数的值。通过使用从概率分布估计装置接收的参数值，异常检测单元计算作为异常行为程度的数据的信息量，以产生异常行为程度。

54. 发明申请

US20060088187A1 Method and apparatus for situation recognition using optical information 有权
标题翻译：使用光学信息进行情景识别的方法和装置
公开(公告)号：US20060088187A1
公开(公告)日：2006-04-27
申请号：US11168667
申请日：2005-06-28
申请人： Brian Clarkson , Makoto Murata , Tamaki Kojima , Wenwu Zhao
发明人： Brian Clarkson , Makoto Murata , Tamaki Kojima , Wenwu Zhao
IPC分类号： G06K9/00
CPC分类号： G06K9/00624 , G06K9/00335 , G06K9/6297
摘要： A situation recognition apparatus includes: an optical information acquisition unit configured to acquire optical information; a storage configured to store a plurality of pieces of optical information; a processing unit configured to match a plurality of the pieces of optical information stored in the storage and optical information newly acquired by the optical information acquisition unit; and an output unit configured to output a result of the matching. The storage further stores a probabilistic model that numerically represents transitions between the plurality of pieces of optical information.
摘要翻译：一种情况识别装置，包括：光信息获取单元，被配置为获取光信息; 存储器，被配置为存储多条光信息; 处理单元，被配置为匹配存储在存储器中的多条光信息和由光信息获取单元新获取的光信息; 以及输出单元，被配置为输出匹配的结果。存储器还存储数字表示多条光信息之间的转换的概率模型。

55. 发明申请

US20060074825A1 System and method for inferring geological classes 有权
标题翻译：推断地质类的系统和方法
公开(公告)号：US20060074825A1
公开(公告)日：2006-04-06
申请号：US10538961
申请日：2004-01-26
申请人： Piotr Mirowski
发明人： Piotr Mirowski
IPC分类号： G06F15/18 , G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G01V1/30 , G01V1/40 , G06K9/6297
摘要： A system for inferring geological classes from oilfield well input data is described using a neural network for inferring class probabilities and class sequencing knowledge and optimising the class probabilities according to the sequencing knowledge.
摘要翻译：使用神经网络来描述从油田井输入数据推断地质类的系统，用于推导类概率和类排序知识，并根据测序知识优化类概率。

56. 发明申请

US20060018541A1 Adaptation of exponential models 有权
标题翻译：指数模型的适应
公开(公告)号：US20060018541A1
公开(公告)日：2006-01-26
申请号：US10977871
申请日：2004-10-29
申请人： Ciprian Chelba , Alejandro Acero
发明人： Ciprian Chelba , Alejandro Acero
IPC分类号： G06K9/00
CPC分类号： G06F17/273 , G06K9/6297
摘要： A method and apparatus are provided for adapting an exponential probability model. In a first stage, a general-purpose background model is built from background data by determining a set of model parameters for the probability model based on a set of background data. The background model parameters are then used to define a prior model for the parameters of an adapted probability model that is adapted and more specific to an adaptation data set of interest. The adaptation data set is generally of much smaller size than the background data set. A second set of model parameters are then determined for the adapted probability model based on the set of adaptation data and the prior model.
摘要翻译：提供了一种适应指数概率模型的方法和装置。在第一阶段，通过基于一组背景数据确定概率模型的一组模型参数，从背景数据构建通用背景模型。背景模型参数然后用于定义适应性概率模型的参数的先验模型，其适应并且更具体于感兴趣的自适应数据集。自适应数据集通常比背景数据集小得多的大小。然后，基于适配数据集和先验模型，针对适应概率模型确定第二组模型参数。

57. 发明授权

US06915241B2 Method for segmentation and identification of nonstationary time series 失效
标题翻译：非平稳时间序列的分割和识别方法
公开(公告)号：US06915241B2
公开(公告)日：2005-07-05
申请号：US10126436
申请日：2002-04-19
申请人： Jens Kohlmorgen , Steven Lemm
发明人： Jens Kohlmorgen , Steven Lemm
IPC分类号： A61B5/048 , G06K9/62 , G06F15/00
CPC分类号： G06K9/6297 , A61B5/048 , A61B5/7264 , G06K9/6228 , G06K9/6232
摘要： A method, implemented on a computer having a fixed amount of memory and CPU resources, for analyzing a sequence of data units derived from a dynamic system to which new data units may be added by classifying the data units, is disclosed. The method comprises determining the similarity of the data units being part of the sequence of data units by calculating the distance between all pairs of data units in a data space. The method further comprises classifying the data units by assigning labels to the data units such that, if the distance of a data unit which is to be classified to any other data unit exceeds a threshold, a new label is assigned to the data unit to be classified. Also, if the threshold is not exceeded, the label of the data unit being closest to the data unit to be classified is assigned to the data unit to be classified.
摘要翻译：公开了一种在具有固定量的存储器和CPU资源的计算机上实现的用于分析从动态系统导出的数据单元序列的方法，通过对数据单元进行分类可以添加新的数据单元。该方法包括通过计算数据空间中所有数据单元对之间的距离来确定作为数据单元序列的一部分的数据单元的相似度。该方法还包括通过向数据单元分配标签来对数据单元进行分类，使得如果待分类到任何其他数据单元的数据单元的距离超过阈值，则将新标签分配给数据单元分类。此外，如果不超过阈值，则将要分类的最接近数据单元的数据单元的标签分配给要分类的数据单元。

58. 发明申请

US20050125223A1 Audio-visual highlights detection using coupled hidden markov models 审中-公开
标题翻译：使用耦合的隐马尔可夫模型的视听亮点检测
公开(公告)号：US20050125223A1
公开(公告)日：2005-06-09
申请号：US10729164
申请日：2003-12-05
申请人： Ajay Divakaran , Ziyou Xiong , Regunathan Radhakrishnan
发明人： Ajay Divakaran , Ziyou Xiong , Regunathan Radhakrishnan
IPC分类号： G06F17/30 , G06K9/00 , G06K9/20 , G06K9/62 , G06T7/00 , G10L11/00 , G10L15/00 , G10L15/04 , G10L15/10 , G10L15/26 , G10L17/00 , G10L19/12 , H04N5/76
CPC分类号： G06K9/00711 , G06F16/739 , G06F16/7834 , G06F16/786 , G06K9/6297
摘要： A method uses probabilistic fusion to detect highlights in videos using both audio and visual information. Specifically, the method uses coupled hidden Markov models (CHMMs). Audio labels are generated using audio classification via Gaussian mixture models (GMMs), and visual labels are generated by quantizing average motion vector magnitudes. Highlights are modeled using discrete-observation CHMMs trained with labeled videos. The CHMMs have better performance than conventional hidden Markov models (HMMs) trained only on audio signals, or only on video frames.
摘要翻译：一种方法使用概率融合来检测使用音频和视觉信息的视频中的高光。具体来说，该方法使用耦合的隐马尔可夫模型（CHMM）。使用高斯混合模型（GMM）的音频分类生成音频标签，并且通过量化平均运动矢量幅度来生成视觉标签。亮点是使用用标记视频训练的离散观察CHMM进行建模。 CHMM具有比仅在音频信号上训练的传统隐马尔可夫模型（HMM）更好的性能，或仅在视频帧上训练。

59. 发明授权

US06801155B2 Method and apparatus for recognising a radar target 失效
标题翻译：用于识别雷达目标的方法和装置
公开(公告)号：US06801155B2
公开(公告)日：2004-10-05
申请号：US10333630
申请日：2003-01-23
申请人： Mohammed Jahangir , Keith M Ponting
发明人： Mohammed Jahangir , Keith M Ponting
IPC分类号： G01S7292
CPC分类号： G06K9/6297 , G01S7/2923 , G01S7/415 , G01S13/524 , G06K9/3241
摘要： A method of recognizing a radar target comprises producing a sequence of Doppler spectra of radar returns form a scene and producing therefrom a sequence of Doppler feature vectors for a target in the scene. Hidden Markov modelling (HMM) is then used to identify the sequence of Doppler feature vectors as indicating a member of a particular class of targets. HMM is used to identify the sequence of Doppler feature vectors by assigning to each feature vector an occurrence probability by selecting a probability distribution or state from a set thereof associated with a class of targets, multiplying the occurrence probabilities together to obtain an overall probability, repeating for other probability distributions in the set to determine a combination of probability distributions giving highest overall probability for that class of target, then repeating for at least one other class of targets and selecting the target class as being that which yields the highest overall occurrence probability.
摘要翻译：识别雷达目标的方法包括从场景产生雷达返回的多普勒频谱序列，并由此产生场景中的目标的多普勒特征向量序列。然后使用隐马尔可夫模型（HMM）来识别多普勒特征向量的序列，以指示特定类别的目标的成员。 HMM用于通过从与特定类别的目标相关联的集合中选择概率分布或状态来向每个特征向量分配发生概率来识别多普勒特征向量的序列，将发生概率相乘以获得总概率，重复对于集合中的其他概率分布来确定给出该类目标的最高总概率的概率分布的组合，然后针对至少一个其他类别的目标重复，并且将目标类别选择为产生最高总发生概率的目标类别。

60. 发明授权

US06683968B1 Method for visual tracking using switching linear dynamic system models 失效
公开(公告)号：US06683968B1
公开(公告)日：2004-01-27
申请号：US09654022
申请日：2000-09-01
申请人： Vladimir Pavlović , James Matthew Rehg
发明人： Vladimir Pavlović , James Matthew Rehg
IPC分类号： G06K900
CPC分类号： G06K9/6297 , G06K9/00335
摘要： A target in a sequence of measurements is tracked by modeling the target with a switching linear dynamic system (SLDS) having a plurality of dynamic models. Each dynamic model is associated with a switching state such that a model is selected when its associated switching state is true. A set of continuous state estimates is determined for a given measurement, and for each possible switching state. A state transition record is then determined by determining and recording, for a given measurement and for each possible switching state, an optimal previous switching state, based on the measurement sequence, where the optimal previous switching state optimizes a transition probability based on the set of continuous state estimates. A measurement model of the target is fitted to the measurement sequence. The measurement model is the description of the influence of the state on the measurement. It couples what is observed to the estimated target. Finally, a trajectory of the target is estimated from the measurement model fitting, the state transition record and parameters of the SLDS, where the estimated trajectory is a sequence of continuous state estimates of the target which correspond to the measurement sequence. The set of continuous state estimates is preferably obtained through Viterbi prediction. The optimal previous switching state can be an optimal prior switching state, or can be an optimal posterior switching state.

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