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公开(公告)号：US09282296B2
公开(公告)日：2016-03-08
申请号：US13693231
申请日：2012-12-04
申请人： Xiang Gao , Vinay Damodar Shet , Xianjun S. Zheng , Sushil Mittal , Mayank Rana , Maneesh Kumar Singh , Bernhard Agthe , Andreas Hutter
发明人： Xiang Gao , Vinay Damodar Shet , Xianjun S. Zheng , Sushil Mittal , Mayank Rana , Maneesh Kumar Singh , Bernhard Agthe , Andreas Hutter
IPC分类号： H04N7/18 , H04N5/77 , G08B13/196
CPC分类号： H04N7/181 , G08B13/19645 , G08B13/1968
摘要： Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.
摘要翻译： 配置多个摄像机用于视频分析。 提供了一个单一的配置工具。 相机之间的相互关系包含在配置内。 使用文本输入字段的组合，将相机注册在地板或其他地图上，并且对来自相机的图像进行标记，可以提供有效的配置工作流程。
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公开(公告)号：US20130147961A1
公开(公告)日：2013-06-13
申请号：US13693231
申请日：2012-12-04
申请人： Xiang Gao , Vinay Damodar Shet , Xianjun S. Zheng , Sushil Mittal , Mayank Rana , Maneesh Kumar Singh , Bernhard Agthe , Andreas Hutter
发明人： Xiang Gao , Vinay Damodar Shet , Xianjun S. Zheng , Sushil Mittal , Mayank Rana , Maneesh Kumar Singh , Bernhard Agthe , Andreas Hutter
IPC分类号： H04N7/18
CPC分类号： H04N7/181 , G08B13/19645 , G08B13/1968
摘要： Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.
摘要翻译： 配置多个摄像机用于视频分析。 提供了一个单一的配置工具。 相机之间的相互关系包含在配置内。 使用文本输入字段的组合，将相机注册在地板或其他地图上，并且对来自相机的图像进行标记，可以提供有效的配置工作流程。
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3.发明申请公开(公告)号：US20120243741A1
公开(公告)日：2012-09-27
申请号：US13397924
申请日：2012-02-16
CPC分类号： G06K9/6296 , G06K2209/09
摘要： A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.
摘要翻译： 一种检测被认为是威胁的图像数据中的对象的方法包括：注释至少一个训练图像的部分，以指示每个部分是否是对象的组件，使用多个第一顺序对描述对象的模式语法进行编码 基于逻辑的谓词规则，训练不同的分量检测器，以基于所标注的训练图像来识别相应的一个分量，使用分量检测器处理图像数据以识别组件中的至少一个，以及执行规则以检测基于对象 对所识别的组件。
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4.发明授权公开(公告)号：US08548231B2
公开(公告)日：2013-10-01
申请号：US12724954
申请日：2010-03-16
申请人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
发明人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
CPC分类号： G06K9/00369 , G06K9/626
摘要： First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.
摘要翻译： 提供了第一级谓词逻辑，扩展了基于双精度的不确定性处理形式，作为正式编码模式语法的手段，解析一组图像特征，并检测在处理器上实现的不同兴趣模式的存在。 来自不同来源的信息和来自检测的不确定性，被集成在双层框架内。 计算机视觉领域中的自动逻辑规则权重学习应用规则权重优化方法，该方法将实例化推理树作为基于知识的神经网络进行聚合，以收敛于在边界框架内提供最佳性能的一组规则权重。 应用是（a）检测部分闭塞下人的存在，（b）检测卫星图像中的大型复杂人造结构（c）检测视频中的时空人类和车辆活动，（c）解析图形用户界面 。
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5.发明申请Predicate Logic based Image Grammars for Complex Visual Pattern Recognition 有权基于逻辑逻辑的图像语法用于复杂视觉模式识别公开(公告)号：US20100278420A1
公开(公告)日：2010-11-04
申请号：US12724954
申请日：2010-03-16
申请人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
发明人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
CPC分类号： G06K9/00369 , G06K9/626
摘要： First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.
摘要翻译： 提供了第一级谓词逻辑，扩展了基于二进制的不确定性处理形式，作为正式编码模式格式的手段，解析一组图像特征，以及检测在处理器上实现的不同感兴趣模式的存在。 来自不同来源的信息和来自检测的不确定性，被集成在双层框架内。 计算机视觉领域中的自动逻辑规则权重学习应用规则权重优化方法，该方法将实例化推理树作为基于知识的神经网络进行聚合，以收敛于在边界框架内提供最佳性能的一组规则权重。 应用是（a）检测部分闭塞下人的存在，（b）检测卫星图像中的大型复杂人造结构（c）检测视频中时空人类和车辆的活动，（c）解析图形用户界面 。
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6.发明授权公开(公告)号：US08903128B2
公开(公告)日：2014-12-02
申请号：US13397924
申请日：2012-02-16
CPC分类号： G06K9/6296 , G06K2209/09
摘要： A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.
摘要翻译： 一种检测被认为是威胁的图像数据中的对象的方法包括：注释至少一个训练图像的部分，以指示每个部分是否是对象的组件，使用多个第一顺序对描述对象的模式语法进行编码 基于逻辑的谓词规则，训练不同的分量检测器，以基于所标注的训练图像来识别相应的一个分量，使用分量检测器处理图像数据以识别组件中的至少一个，以及执行规则以检测基于对象 对所识别的组件。
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7.发明授权公开(公告)号：US09117147B2
公开(公告)日：2015-08-25
申请号：US13451845
申请日：2012-04-20
IPC分类号： H04N5/225 , G06K9/68 , G06T7/20 , G08B13/196
CPC分类号： G06K9/6857 , G06T7/277 , G06T2207/20081 , G06T2207/30196 , G06T2207/30232 , G06T2207/30241 , G08B13/19608
摘要： A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt−1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt−1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detection hypotheses for the video sequence.
摘要翻译： 一种用于跟踪视频序列中的行人的方法，其中视频序列的每个图像帧对应于时间步长，包括使用边缘空间学习来采样给定的多人身份分配的先验概率分布p（xt | Zt-1） 一组来自所有先前图像帧的特征测量，使用边缘空间学习来估计给定从先验概率分布采样的一组多人身份分配的特征集合的观察似然分布p（zt | xt），计算 根据观察似然分布p（zt | xt）和先验概率分布p（xt | Zt-1）的后验概率分布p（xt | Zt），并利用边际空间学习估计先验概率分布p（xt + | Zt），给出后验概率分布p（xt | Zt）和概率p（xt + 1 | xt）的下一图像帧，其中多人身份分配的后验概率分布对应于 视频序列的一组行人检测假设。
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8.发明申请公开(公告)号：US20120274781A1
公开(公告)日：2012-11-01
申请号：US13451845
申请日：2012-04-20
CPC分类号： G06K9/6857 , G06T7/277 , G06T2207/20081 , G06T2207/30196 , G06T2207/30232 , G06T2207/30241 , G08B13/19608
摘要： A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt−1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt−1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detection hypotheses for the video sequence.
摘要翻译： 一种用于跟踪视频序列中的行人的方法，其中视频序列的每个图像帧对应于时间步长，包括使用边缘空间学习来采样给定的多人身份分配的先验概率分布p（xt | Zt-1） 一组来自所有先前图像帧的特征测量，使用边缘空间学习来估计给定从先验概率分布采样的一组多人身份分配的特征集合的观察似然分布p（zt | xt），计算 根据观察似然分布p（zt | xt）和先验概率分布p（xt | Zt-1）的后验概率分布p（xt | Zt），并利用边际空间学习估计先验概率分布p（xt + | Zt），给出后验概率分布p（xt | Zt）和概率p（xt + 1 | xt）的下一图像帧，其中多人身份分配的后验概率分布对应于 视频序列的一组行人检测假设。
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公开(公告)号：US20120259885A1
公开(公告)日：2012-10-11
申请号：US13442124
申请日：2012-04-09
IPC分类号： G06F17/30
CPC分类号： G06F17/30734
摘要： A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.
摘要翻译： 一种用于检索分布在多个不同本体上的信息的方法，包括：定义元本体，其中所述元本体包括高级属性及其与在多个不同本体中定义的特定属性的映射; 接收问题，其中该问题与高级别财产相关联; 并提供对该问题的答案，其中通过使用元本体来确定答案。
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公开(公告)号：US09183294B2
公开(公告)日：2015-11-10
申请号：US13442124
申请日：2012-04-09
IPC分类号： G06F17/30
CPC分类号： G06F17/30734
摘要： A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.
摘要翻译： 一种用于检索分布在多个不同本体上的信息的方法，包括：定义元本体，其中所述元本体包括高级属性及其与在多个不同本体中定义的特定属性的映射; 接收问题，其中该问题与高级别财产相关联; 并提供对该问题的答案，其中通过使用元本体来确定答案。
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