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。

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。

公开(公告)号：US08018874B1

公开(公告)日：2011-09-13

申请号：US12387752

申请日：2009-05-06

申请人： Yuri Owechko

发明人： Yuri Owechko

IPC分类号： H04L12/28

CPC分类号： H04L41/0823 ,  H04W4/08 ,  H04W28/18

摘要： Described is a distributed network optimization system implementing distributed particle swarm optimization, which allows multiple nodes to cooperate in searching efficiently for a set of parameter values that optimizes overall network performance. The system comprises a multi-dimensional network parameter space with software agents configured to operate as a cooperative swarm to locate an objective function optima. The software agents are individually distributed across multiple nodes in the network, and each node processes a portion of each software agent to obtain information regarding the local performance of the software agent. A global measure of network performance is then computed based on sharing of local performance information between nodes, which each node uses to adjust its parameters accordingly. In this manner, the global utility of the network can be optimized using local processing only. Also described is a method of utilizing the system.

摘要翻译： 描述了实现分布式粒子群优化的分布式网络优化系统，其允许多个节点协作以有效地搜索一组优化整体网络性能的参数值。 该系统包括多维网络参数空间，其中软件代理被配置为作为协作群进行操作以定位目标函数最优。 软件代理单独地分布在网络中的多个节点上，并且每个节点处理每个软件代理的一部分以获得关于软件代理的本地性能的信息。 然后基于节点之间的本地性能信息的共享来计算网络性能的全局度量，每个节点用于相应地调整其参数。 以这种方式，可以仅使用本地处理来优化网络的全局效用。 还描述了利用该系统的方法。

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。

公开(公告)号：US20090069027A1

公开(公告)日：2009-03-12

申请号：US11854384

申请日：2007-09-12

申请人： Eddie R. Brock ,  Thomas G. Ribardo, JR. ,  Darrell L. Young ,  Yuri Owechko

发明人： Eddie R. Brock ,  Thomas G. Ribardo, JR. ,  Darrell L. Young ,  Yuri Owechko

IPC分类号： H04Q7/20

CPC分类号： G01S7/412 ,  G01S13/0209 ,  G01S13/88

摘要： In one embodiment, a system for identifying a communication device includes a signal generator coupled to a transmit horn and a computing system coupled to a receive horn through a receiver. The signal generator is operable to generate an excitation waveform from the transmit horn such that the communication device passively reflects a response waveform. The computing system is operable to receive the response waveform from the communication device and compare the response waveform to a plurality of reference waveforms to determine the identity of the communication device.

摘要翻译： 在一个实施例中，用于识别通信设备的系统包括耦合到发射喇叭的信号发生器和通过接收器耦合到接收喇叭的计算系统。 信号发生器可操作以从发射喇叭产生激励波形，使得通信设备被动地反映响应波形。 计算系统可操作以从通信设备接收响应波形，并将响应波形与多个参考波形进行比较，以确定通信设备的身份。

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。

公开(公告)号：US20070063854A1

公开(公告)日：2007-03-22

申请号：US11195469

申请日：2005-08-02

申请人： Jing Zhang ,  Yilu Zhang ,  Yuri Owechko

发明人： Jing Zhang ,  Yilu Zhang ,  Yuri Owechko

IPC分类号： G08B23/00

CPC分类号： B60W40/09 ,  B60W2050/0029

摘要： A method for adaptive driver workload estimation. A subjective assessment of a driver workload is received from a vehicle driver. A stream of sensor input data is collected from one or more sensors in response to receiving the subjective assessment. A machine learning algorithm is applied to a driver workload estimate model based on the stream of sensor input data and the subjective assessment. The result of the applying is an updated driver workload estimate model.

摘要翻译： 一种用于自适应驱动程序工作量估计的方法。 从车辆驾驶员接收到驾驶员工作量的主观评估。 响应于接收主观评估，从一个或多个传感器收集传感器输入数据流。 基于传感器输入数据流和主观评估的机器学习算法应用于驾驶员工作量估计模型。 应用的结果是更新的驱动程序工作负载估计模型。

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。

公开(公告)号：US20050169529A1

公开(公告)日：2005-08-04

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 ,  G06K9/46 ,  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。

公开(公告)号：US06608910B1

公开(公告)日：2003-08-19

申请号：US09389874

申请日：1999-09-02

申请人： Narayan Srinivasa ,  Yuri Owechko

发明人： Narayan Srinivasa ,  Yuri Owechko

IPC分类号： G06K900

CPC分类号： G06K9/00362 ,  G06K9/3241

摘要： An computer vision method and system for recognizing and tracking occupants in a fixed space under variable illumination. The system utilizes a camera to capture an initial image of the unoccupied fixed space and subsequently captures images of the occupied fixed space. The edge maps of the current estimate of the unoccupied fixed space including illumination variations and the occupied fixed space are computed. The edge map of the current estimate of the unoccupied fixed space is then subtracted from the edge map of the occupied fixed space to yield a residual edge map, which is then processed to extract the image of the occupant. At least one equivalent rectangle is then computed from the two-dimensional moments of the image of the occupant. The equivalent rectangles are then used to determine the occupant type and position and to track changes in real-time. This method and system is generally designed for use with automobile safety systems such as “smart” airbags. However, it may be tailored to many applications such as computer gaming, adaptive automotive controls, and “smart” homes, among others.

摘要翻译： 一种用于在可变照明下识别和跟踪固定空间中的乘客的计算机视觉方法和系统。 该系统利用相机捕获未占用的固定空间的初始图像，并随后捕获被占用的固定空间的图像。 计算包括照明变化和占用固定空间在内的未占用固定空间的当前估计的边缘图。 然后从占用的固定空间的边缘图中减去未占用的固定空间的当前估计的边缘图，以产生剩余边缘图，然后对其进行处理以提取乘员的图像。 然后从乘员的图像的二维矩中计算至少一个等效的矩形。 然后使用等效矩形来确定乘员类型和位置并实时跟踪变化。 该方法和系统通常设计用于诸如“智能”安全气囊之类的汽车安全系统。 然而，它可以针对诸如计算机游戏，适应性汽车控制和“智能”家庭等众多应用。