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21. 发明申请

US20060129843A1 Method and apparatus for electronically extracting application specific multidimensional information from documents selected from a set of documents electronically extracted from a library of electronically searchable documents 审中-公开
公开(公告)号：US20060129843A1
公开(公告)日：2006-06-15
申请号：US11198798
申请日：2005-08-05
申请人： Narayan Srinivasa , Swarup Medasani , Yuri Owechko , Deepak Khosla
发明人： Narayan Srinivasa , Swarup Medasani , Yuri Owechko , Deepak Khosla
IPC分类号： G06F12/14
CPC分类号： G06F16/335 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： An apparatus and method is disclosed for providing application specific multi-dimensional information to an application running on a user computing device, wherein at least one dimension of the information is a category, from a plurality of member documents electronically extracted from a library of electronically searchable documents, which may comprise an application specific multidimensional information extractor adapted to extract occurrences of prospective representations of dimensions of application specific multidimensional information from the member documents, and to extract occurrences of non-application specific multidimensional information from the member documents; and, an encoder adapted to encode the occurrences of prospective dimensions of application specific multidimensional information and non-application specific multidimensional information contained in member documents according to a dimension specific coded representation of each dimension of application specific multidimensional information and a non-application specific coded representation of each non-application specific multidimensional information element. The apparatus and method may further comprise a member document identifier adapted to determine whether a member document contains coded formatting, and if not, whether the member document is a dense document, and if not, for rejecting the document from further processing, and the coded formatting may comprise network markup language coding. The apparatus and method may further comprise an application specific multidimensional information verification unit adapted verify the extraction of application specific multi-dimensional information from the member documents, and may further comprise a database for storing the application specific multi-dimensional information adapted to provide an application running on a user computing device access to the application specific multidimensional information. The application specific multidimensional information may be scheduled events having the dimensions of time, location and event identity, and the application running on the user computer can be an electronic calendar or other similar scheduling software program.

22. 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。

23. 发明授权

US08798372B1 Method for detecting bridges using lidar point cloud data 有权
标题翻译：使用激光雷达云数据检测桥梁的方法
公开(公告)号：US08798372B1
公开(公告)日：2014-08-05
申请号：US13414391
申请日：2012-03-07
申请人： Dmitriy Korchev , Swarup Medasani , Yuri Owechko
发明人： Dmitriy Korchev , Swarup Medasani , Yuri Owechko
IPC分类号： G06K9/46
CPC分类号： G06K9/00637 , G06K9/00651 , G06K9/6211
摘要： Described is a system and method for detecting elevated structures, such as bridges and overpasses, in point cloud data. A set of data from a three-dimensional point cloud of a landscape is received by the system. The set of data points comprises inlier data points and outlier data points. The inlier data points in the three-dimensional point cloud data are identified and combined into at least one segment. The segment is converted into an image comprising at least one image level. Each image level is processed with an edge detection algorithm to detect elevated edges. The elevated edges are vectorized to identify an elevated structure of interest in the landscape. The present invention is useful in applications that require three-dimensional sensing systems, such as autonomous navigation and surveillance applications.
摘要翻译：描述了用于在点云数据中检测诸如桥梁和立交桥的升高结构的系统和方法。由系统接收来自景观的三维点云的一组数据。数据点集合包括异常数据点和异常值数据点。三维点云数据中的上位数据点被识别并组合成至少一个段。该片段被转换成包括至少一个图像级别的图像。每个图像级别都使用边缘检测算法进行处理，以检测提升的边缘。提升的边缘被矢量化以识别景观中兴趣兴高的结构。本发明在需要诸如自主导航和监视应用的三维感测系统的应用中是有用的。

24. 发明授权

US08744200B1 Hybrid compressive/Nyquist sampling for enhanced sensing 有权
标题翻译：混合压缩/奈奎斯特采样用于增强感测
公开(公告)号：US08744200B1
公开(公告)日：2014-06-03
申请号：US13464887
申请日：2012-05-04
申请人： Yuri Owechko , Swarup Medasani
发明人： Yuri Owechko , Swarup Medasani
IPC分类号： G06K9/54 , G06K9/60
CPC分类号： H04N5/23245 , G06K9/00496 , H04N5/23229 , H04N19/136 , H04N19/194
摘要： Described is a knowledge-enhanced compressive imaging system. The system first initializes a compressive measurement basis set and a measurement matrix using task- and scene-specific prior knowledge. An image captured using the imaging mode of the dual-mode sensor is then sampled to extract context knowledge. The compressive measurement basis set and the measurement matrix are adapted using the extracted context knowledge and the prior knowledge. Task-relevant compressive measurements of the image are performed using the compressive measurement mode of the dual-mode sensor, and compressive reconstruction of the image is performed. Finally, a task and context optimized signal representation of the image is generated.
摘要翻译：描述了一种知识增强的压缩成像系统。系统首先使用任务和场景特定的先验知识初始化压缩测量基础集和测量矩阵。然后对使用双模传感器的成像模式捕获的图像进行采样以提取上下文知识。使用提取的上下文知识和现有知识来调整压缩测量基础集和测量矩阵。使用双模传感器的压缩测量模式执行图像的任务相关的压缩测量，并且执行图像的压缩重建。最后，生成图像的任务和上下文优化的信号表示。

25. 发明授权

US08589315B2 Behavior recognition using cognitive swarms and fuzzy graphs 有权
标题翻译：使用认知群体和模糊图的行为识别
公开(公告)号：US08589315B2
公开(公告)日：2013-11-19
申请号：US11800264
申请日：2007-05-03
申请人： Swarup Medasani , Yuri Owechko
发明人： Swarup Medasani , Yuri Owechko
IPC分类号： G06F15/18 , G06F15/00
CPC分类号： G06K9/6292 , G06K9/00369 , G06K9/6229
摘要： Described is a behavior recognition system for detecting the behavior of objects in a scene. The system comprises a semantic object stream module for receiving a video stream having at least two frames and detecting objects in the video stream. Also included is a group organization module for utilizing the detected objects from the video stream to detect a behavior of the detected objects. The group organization module further comprises an object group stream module for spatially organizing the detected objects to have relative spatial relationships. The group organization module also comprises a group action stream module for modeling a temporal structure of the detected objects. The temporal structure is an action of the detected objects between the two frames, whereby through detecting, organizing and modeling actions of objects, a user can detect the behavior of the objects.
摘要翻译：描述了用于检测场景中的对象的行为的行为识别系统。该系统包括语义对象流模块，用于接收具有至少两个帧的视频流并检测视频流中的对象。还包括用于利用来自视频流的检测对象的组织组织模块来检测检测到的对象的行为。组织模块还包括用于空间组织所检测到的对象以具有相对空间关系的对象组流模块。组织模块还包括用于对所检测到的对象的时间结构建模的组动作流模块。时间结构是两帧之间检测到的对象的动作，由此通过检测，组织和建模对象的动作，用户可以检测对象的行为。

26. 发明授权

US08488877B1 System for object recognition in colorized point clouds 有权
标题翻译：彩色点云中物体识别系统
公开(公告)号：US08488877B1
公开(公告)日：2013-07-16
申请号：US12592836
申请日：2009-12-02
申请人： Yuri Owechko , Swarup Medasani , Ronald T. Azuma , Jim Nelson
发明人： Yuri Owechko , Swarup Medasani , Ronald T. Azuma , Jim Nelson
IPC分类号： G06K9/00
CPC分类号： G06K9/00201 , G06K9/00704
摘要： Described is a system for object recognition in colorized point clouds. The system includes an implicit geometry engine that is configured to receive three-dimensional (3D) colorized cloud point data regarding a 3D object of interest and to convert the cloud point data into implicit representations. The engine also generates geometric features. A geometric grammar block is included to generate object cues and recognize geometric objects using geometric tokens and grammars based on object taxonomy. A visual attention cueing block is included to generate object cues based on 3D geometric properties. Finally, an object recognition block is included to perform a local search for objects using cues from the cueing block and the geometric grammar block and to classify the 3D object of interest as a particular object upon a classifier reaching a predetermined threshold.
摘要翻译：描述了在彩色点云中的对象识别系统。该系统包括隐式几何引擎，其被配置为接收关于感兴趣的3D对象的三维（3D）着色浊点数据，并将该浊点数据转换为隐含表示。引擎还生成几何特征。包含几何语法块以生成对象提示，并使用基于对象分类法的几何令牌和语法来识别几何对象。包括视觉注意提示块，以根据3D几何属性生成对象提示。最后，包括对象识别块，以使用来自提示块和几何语法块的提示来执行对象的本地搜索，并且在分类器达到预定阈值时将感兴趣的3D对象分类为特定对象。

27. 发明授权

US08213709B1 Method and system for directed area search using cognitive swarm vision and cognitive Bayesian reasoning 有权
标题翻译：使用认知群体视觉和认知贝叶斯推理的定向区域搜索的方法和系统
公开(公告)号：US08213709B1
公开(公告)日：2012-07-03
申请号：US12590110
申请日：2009-11-03
申请人： Swarup Medasani , Yuri Owechko , Tsai-Ching Lu , Deepak Khosla , David L. Allen
发明人： Swarup Medasani , Yuri Owechko , Tsai-Ching Lu , Deepak Khosla , David L. Allen
IPC分类号： G06K9/62
CPC分类号： G06K9/6278 , G06K9/00671 , G06K9/6263 , G06N7/005
摘要： A method and system for a directed area search using cognitive swarm vision and cognitive Bayesian reasoning is disclosed. The system comprises a domain knowledge database, a top-down reasoning module, and a bottom-up module. The domain knowledge database is configured to store Bayesian network models comprising visual features and observables associated with various sets of entities. The top-down module is configured to receive a search goal, generate a plan of action using Bayesian network models, and partition the plan into a set of tasks/observables to be located in the imagery. The bottom-up module is configured to select relevant feature/attention models for the observables, and search the visual imagery using a cognitive swarm for the at least one observable. The system further provides for operator feedback and updating of the domain knowledge database to perform better future searches.
摘要翻译：公开了一种使用认知群体视觉和认知贝叶斯推理的定向区域搜索的方法和系统。该系统包括域知识数据库，自上而下推理模块和自下而上模块。域知识数据库被配置为存储包括与各组实体相关联的视觉特征和可观察性的贝叶斯网络模型。自顶向下模块被配置为接收搜索目标，使用贝叶斯网络模型生成行动计划，并将计划分成一组要在图像中的任务/可观察值。自下而上模块被配置为选择可观察的相关特征/关注模型，并且使用用于至少一个可观察的认知群搜索视觉图像。该系统进一步提供操作者反馈和更新领域知识数据库以执行更好的未来搜索。

28. 发明授权

US07587064B2 Active learning system for object fingerprinting 有权
标题翻译：主体学习系统用于对象指纹识别
公开(公告)号：US07587064B2
公开(公告)日：2009-09-08
申请号：US11051860
申请日：2005-02-03
申请人： Yuri Owechko , Swarup Medasani , Narayan Srinivasa
发明人： Yuri Owechko , Swarup Medasani , Narayan Srinivasa
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/469 , G06K9/6254
摘要： Described is an active learning system for fingerprinting an object identified in an image frame. The active learning system comprises a flow-based object segmentation module for segmenting a potential object candidate from a video sequence, a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate, a static classifier for initial classification of the potential object candidate, an incremental learning module for predicting a general class of the potential object candidate, an oriented localized filter module to extract features from the potential object candidate, and a learning-feature graph-fingerprinting module configured to receive the features and build a fingerprint of the object for tracking the object.
摘要翻译：描述了一种用于对在图像帧中识别的对象进行指纹识别的主动学习系统。主动学习系统包括：基于流的对象分割模块，用于从视频序列分割潜在对象候选者;使用哈尔小波的固定基函数分解模块从潜在对象候选者提取相关特征集;初始化的静态分类器潜在对象候选者的分类，用于预测潜在候选对象的一般类别的增量学习模块，从潜在对象候选者提取特征的定向局部化过滤器模块，以及被配置为接收特征的学习特征图指纹模块并构建对象的指纹以跟踪对象。

29. 发明申请

US20070263900A1 Behavior recognition using cognitive swarms and fuzzy graphs 有权
标题翻译：使用认知群体和模糊图的行为识别
公开(公告)号：US20070263900A1
公开(公告)日：2007-11-15
申请号：US11800264
申请日：2007-05-03
申请人： Swarup Medasani , Yuri Owechko
发明人： Swarup Medasani , Yuri Owechko
IPC分类号： G06K9/00
CPC分类号： G06K9/6292 , G06K9/00369 , G06K9/6229
摘要： Described is a behavior recognition system for detecting the behavior of objects in a scene. The system comprises a semantic object stream module for receiving a video stream having at least two frames and detecting objects in the video stream. Also included is a group organization module for utilizing the detected objects from the video stream to detect a behavior of the detected objects. The group organization module further comprises an object group stream module for spatially organizing the detected objects to have relative spatial relationships. The group organization module also comprises a group action stream module for modeling a temporal structure of the detected objects. The temporal structure is an action of the detected objects between the two frames, whereby through detecting, organizing and modeling actions of objects, a user can detect the behavior of the objects.
摘要翻译：描述了用于检测场景中的对象的行为的行为识别系统。该系统包括语义对象流模块，用于接收具有至少两个帧的视频流并检测视频流中的对象。还包括用于利用来自视频流的检测对象的组织组织模块来检测检测到的对象的行为。组织模块还包括用于空间组织所检测到的对象以具有相对空间关系的对象组流模块。组织模块还包括用于对所检测到的对象的时间结构建模的组动作流模块。时间结构是两帧之间检测到的对象的动作，由此通过检测，组织和建模对象的动作，用户可以检测对象的行为。

30. 发明申请

US20050196047A1 Object recognition system incorporating swarming domain classifiers 有权
标题翻译：包含群体域分类器的对象识别系统
公开(公告)号：US20050196047A1
公开(公告)日：2005-09-08
申请号：US10918336
申请日：2004-08-14
申请人： Yuri Owechko , Swarup Medasani
发明人： Yuri Owechko , Swarup Medasani
IPC分类号： G06E1/00 , G06K9/00 , G06K9/62
CPC分类号： G06N5/043 , G06K9/00369 , G06K9/6229
摘要： The present invention relates to a system, method, and computer program product for recognition objects in a domain which combines feature-based object classification with efficient search mechanisms based on swarm intelligence. The present invention utilizes a particle swarm optimization (PSO) algorithm and a possibilistic particle swarm optimization algorithm (PPSO), which are effective for optimization of a wide range of functions. PSO searches a multi-dimensional solution space using a population of “software agents” in which each software agent has its own velocity vector. PPSO allows different groups of software agents (i.e., particles) to work together with different temporary search goals that change in different phases of the algorithm. Each agent is a self-contained classifier that interacts and cooperates with other classifier agents to optimize the classifier confidence level. By performing this optimization, the swarm simultaneously finds objects in the scene, determines their size, and optimizes the classifier parameters.
摘要翻译：本发明涉及用于识别域中的对象的系统，方法和计算机程序产品，其中基于特征的对象分类与基于群体智能的有效搜索机制相结合。本发明利用粒子群优化（PSO）算法和可能的粒子群优化算法（PPSO），这对于优化广泛的功能是有效的。 PSO使用大量“软件代理”搜索多维解决方案空间，其中每个软件代理具有其自己的速度向量。 PPSO允许不同组的软件代理（即，粒子）与在算法的不同阶段中改变的不同临时搜索目标协同工作。每个代理是一个自包含的分类器，与其他分类器代理进行交互和协作，以优化分类器置信水平。通过执行此优化，群同时查找场景中的对象，确定其大小，并优化分类器参数。

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