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1. 发明授权

US08270671B1 Method and system for generic object detection using block features 失效
标题翻译：使用块特征的通用对象检测的方法和系统
公开(公告)号：US08270671B1
公开(公告)日：2012-09-18
申请号：US12380415
申请日：2009-02-27
申请人： Swarup Medasani , Rahul Shringarpure
发明人： Swarup Medasani , Rahul Shringarpure
IPC分类号： G06K9/00
CPC分类号： G06K9/00986 , G06K9/4642 , G06K9/6202
摘要： Disclosed is a method and system for generic object detection using block-based feature computation and, more specifically, a method and system for massively parallel computation of object features sets according to an optimized clock-cycle matrix. The method uses an array of correlators to calculate block sums for each section of the image to be analyzed. A greedy heuristic scheduling algorithm is executed to produce an optimized clock cycle matrix such that overlapping features which use the same block sum do not attempt to access the block at the same time, thereby avoiding race memory conditions. The processing system can employ any of a variety of hardwired Very Large Scale Integration (VLSI) chips such as Field Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs) and Application Specific Integrated Circuits (ASICs).
摘要翻译：公开了一种使用基于块的特征计算的通用对象检测的方法和系统，更具体地，涉及根据优化的时钟周期矩阵对对象特征集进行大规模并行计算的方法和系统。该方法使用相关器阵列来计算待分析图像的每个部分的块和。执行贪婪启发式调度算法以产生优化的时钟周期矩阵，使得使用相同块和的重叠特征不尝试同时访问块，从而避免竞态存储器条件。处理系统可以采用各种硬连线超大规模集成（VLSI）芯片，例如现场可编程门阵列（FPGA），数字信号处理器（DSP）和专用集成电路（ASIC）。

2. 发明授权

US07761389B2 Method for anomaly prediction of battery parasitic load 失效
标题翻译：电池寄生负载异常预测方法
公开(公告)号：US07761389B2
公开(公告)日：2010-07-20
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译：电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。

3. 发明授权

US07715591B2 High-performance sensor fusion architecture 有权
标题翻译：高性能传感器融合架构
公开(公告)号：US07715591B2
公开(公告)日：2010-05-11
申请号：US10132875
申请日：2002-04-24
申请人： Yuri Owechko , Narayan Srinivasa , Swarup Medasani , Riccardo Boscolo
发明人： Yuri Owechko , Narayan Srinivasa , Swarup Medasani , Riccardo Boscolo
IPC分类号： G06K9/00
CPC分类号： G06K9/6292 , G06K9/00201 , G06K9/00369 , G06K9/00832 , G06K9/209 , G06K9/522 , G06T7/97 , G06T2207/10021 , G06T2207/20064 , G06T2207/30268 , H04N2013/0081 , H04N2013/0092
摘要： A vision-based system for automatically detecting the type of object within a specified area, such as the type of occupant within a vehicle is presented. The type of occupant can then be used to determine whether an airbag deployment system should be enabled or not. The system extracts different features, including wavelet features and/or a disparity map from images captured by image sensors. These features are then processed by classification algorithms to produce class confidences for various occupant types. The occupant class confidences are fused and processed to determine occupant type. In a preferred embodiment, image features from image edges, wavelet features, and disparity are used. Various classification algorithms may be implemented to classify the object. Use of the disparity map and/or wavelet features provides greater computational efficiency.
摘要翻译：提出了一种基于视觉的系统，用于自动检测指定区域内物体的类型，例如车辆内乘客的类型。然后可以使用乘客的类型来确定是否应启用安全气囊展开系统。该系统从图像传感器捕获的图像中提取不同的特征，包括小波特征和/或视差图。然后通过分类算法对这些特征进行处理，以便为各种乘客类型生成类别信息。乘员班信心被融合和处理以确定乘客类型。在优选实施例中，使用来自图像边缘的图像特征，小波特征和视差。可以实现各种分类算法来对对象进行分类。使用视差图和/或小波特征提供更大的计算效率。

4. 发明申请

US20070183670A1 Graph-based cognitive swarms for object group recognition 有权
标题翻译：基于图的认知群体，用于对象组识别
公开(公告)号：US20070183670A1
公开(公告)日：2007-08-09
申请号：US11433159
申请日：2006-05-12
申请人： Yuri Owechko , Swarup Medasani
发明人： Yuri Owechko , Swarup Medasani
IPC分类号： G06K9/62 , G06K9/46
CPC分类号： G06K9/6292 , G06K9/00369 , G06K9/6229
摘要： An object recognition system is described that incorporates swarming classifiers. The swarming classifiers comprise a plurality of software agents configured to operate as a cooperative swarm to classify an object group in a domain. Each node N represents an object in the group having K object attributes. Each agent is assigned an initial velocity vector to explore a KN-dimensional solution space for solutions matching the agent's graph. Further, each agent is configured to search the solution space for an optimum solution. The agents keep track of their coordinates in the KN-dimensional solution space that are associated with an observed best solution (pbest) and a global best solution (gbest). The gbest is used to store the best solution among all agents which corresponds to a best graph among all agents. Each velocity vector thereafter changes towards pbest and gbest, allowing the cooperative swarm to classify of the object group.
摘要翻译：描述了包含群组分类器的对象识别系统。群集分类器包括被配置为作为协作群进行操作以将域中的对象组分类的多个软件代理。每个节点N表示具有K个对象属性的组中的对象。为每个代理分配一个初始速度向量，以探索与代理图相匹配的解决方案的KN维解决方案空间。此外，每个代理被配置为搜索解空间以获得最佳解决方案。代理人跟踪与观察到的最佳解决方案（pbest）和全局最佳解决方案（gbest）相关联的KN维解决方案空间中的坐标。 gbest用于在所有代理之间存储对应于最佳图形的所有代理中的最佳解决方案。其后每个速度矢量向pbest和gbest变化，允许协作群对目标群进行分类。

5. 发明授权

US08874584B1 Hierarchical video search and recognition system 有权
标题翻译：分层视频搜索和识别系统
公开(公告)号：US08874584B1
公开(公告)日：2014-10-28
申请号：US12660320
申请日：2010-02-24
申请人： Yang Chen , Swarup Medasani , David L. Allen , Qin Jiang , Yuri Owechko , Tsai-Ching Lu
发明人： Yang Chen , Swarup Medasani , David L. Allen , Qin Jiang , Yuri Owechko , Tsai-Ching Lu
IPC分类号： G06F17/30
CPC分类号： G06F17/30805 , G06F17/30811
摘要： Described is a system for content recognition, search, and retrieval in visual data. The system is configured to perform operations of receiving visual data as an input, processing the visual data, and extracting distinct activity-agnostic content descriptors from the visual data at each level of a hierarchical content descriptor module. The resulting content descriptors are then indexed with a hierarchical content indexing module, wherein each level of the content indexing module comprises a distinct set of indexed content descriptors. The visual data, generated content descriptors, and indexed content descriptors are then stored in a storage module. Finally, based on a content-based query by a user, the storage module is searched, and visual data containing the content of interest is retrieved and presented to the user. A method and computer program product for content recognition, search, and retrieval in visual data are also described.
摘要翻译：描述了用于视觉数据中的内容识别，搜索和检索的系统。该系统被配置为执行接收视觉数据作为输入，处理可视数据以及从分层内容描述符模块的每个级别的视觉数据中提取不同的活动不可知内容描述符的操作。所得到的内容描述符然后用分层内容索引模块进行索引，其中内容索引模块的每个级别包括不同的索引内容描述符集合。然后将可视数据，生成的内容描述符和索引的内容描述符存储在存储模块中。最后，基于用户的基于内容的查询，搜索存储模块，并且检索包含感兴趣内容的视觉数据并呈现给用户。还描述了用于视觉数据中的内容识别，搜索和检索的方法和计算机程序产品。

6. 发明授权

US08818036B1 Contextual behavior state filter for sensor registration 有权
标题翻译：用于传感器注册的上下文行为状态过滤器
公开(公告)号：US08818036B1
公开(公告)日：2014-08-26
申请号：US13558257
申请日：2012-07-25
申请人： Yuri Owechko , Shinko Y. Cheng , Swarup Medasani , Kyungnam Kim
发明人： Yuri Owechko , Shinko Y. Cheng , Swarup Medasani , Kyungnam Kim
IPC分类号： G06K9/00
CPC分类号： G06K9/00771 , G06K9/00369 , G06K9/209 , G06T7/277 , G06T2207/10016 , G06T2207/20076 , G06T2207/30232 , G06T2207/30241
摘要： Described is a system for registering a viewpoint of an imaging sensor with respect to a geospatial model or map. An image of a scene of a geospatial region comprising an object is received as input. The image of the scene is captured by a sensor having a current sensor state. Observation data related to the object's state is received, wherein the observation data comprises an object behavior of the object given the geospatial region. An estimate of the current sensor state is generated using a probability of an observation from the observation data given the current sensor state x. Finally, the image of the scene is registered with a geospatial model or map based on the estimate of the current sensor state.
摘要翻译：描述了一种用于记录关于地理空间模型或地图的成像传感器的视点的系统。接收包括对象的地理空间区域的场景的图像作为输入。场景的图像由具有当前传感器状态的传感器捕获。接收与对象状态相关的观测数据，其中观测数据包括给定地理空间区域的对象的对象行为。使用给定当前传感器状态x的观测数据的观察概率来生成当前传感器状态的估计。最后，基于当前传感器状态的估计，将场景的图像与地理空间模型或地图一起注册。

7. 发明授权

US08437558B1 Vision-based method for rapid directed area search 有权
标题翻译：用于快速指导区域搜索的基于视觉的方法
公开(公告)号：US08437558B1
公开(公告)日：2013-05-07
申请号：US12587642
申请日：2009-10-08
申请人： Swarup Medasani , Yuri Owechko
发明人： Swarup Medasani , Yuri Owechko
IPC分类号： G06K9/00 , G06K9/62 , G06K9/34
CPC分类号： G06K9/6211 , G06K9/4671
摘要： Described is a system for rapid directed area search utilizing particle swarm optimization. The system first extracts salient regions from an input image. The system then detects regions of interest from the salient regions utilizing particle swarm optimization, wherein a swarm of software agents, or particles, cooperate to locate an objective function optima, or region of interest, in an image. A set of local feature descriptors are then extracted from the image, wherein a local feature descriptor corresponds to a neighborhood surrounding a point of interest in a region of interest in the image. Additionally, the set of local feature descriptors are clustered hierarchically into a database so that a closest match between a new input image and a stored image can be determined. Finally, the matching regions of the two images are registered to align matching regions to allow detection of changes between the images.
摘要翻译：描述了利用粒子群优化的快速定向区域搜索系统。系统首先从输入图像中提取突出区域。系统然后使用粒子群优化来检测来自显着区域的感兴趣区域，其中，一组软件代理或粒子协作以在图像中定位目标函数最佳值或感兴趣区域。然后从图像中提取一组局部特征描述符，其中局部特征描述符对应于围绕图像中感兴趣区域中的兴趣点的邻域。另外，本地特征描述符的集合被分层地分组成数据库，使得可以确定新的输入图像和存储的图像之间的最接近的匹配。最后，两个图像的匹配区域被注册以对准匹配区域以允许检测图像之间的变化。

8. 发明授权

US08433098B1 Method and system for generic object detection using block features 失效
标题翻译：使用块特征的通用对象检测的方法和系统
公开(公告)号：US08433098B1
公开(公告)日：2013-04-30
申请号：US13535098
申请日：2012-06-27
申请人： Swarup Medasani , Rahul Shringarpure
发明人： Swarup Medasani , Rahul Shringarpure
IPC分类号： G06K9/00
CPC分类号： G06K9/00986 , G06K9/4642 , G06K9/6202
摘要： Disclosed is a method and system for generic object detection using block-based feature computation and, more specifically, a method and system for massively parallel computation of object features sets according to an optimized clock-cycle matrix. The method uses an array of correlators to calculate block sums for each section of the image to be analyzed. A greedy heuristic scheduling algorithm is executed to produce an optimized clock cycle matrix such that overlapping features which use the same block sum do not attempt to access the block at the same time, thereby avoiding race memory conditions. The processing system can employ any of a variety of hardwired Very Large Scale Integration (VLSI) chips such as Field Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs) and Application Specific Integrated Circuits (ASICs).
摘要翻译：公开了一种使用基于块的特征计算的通用对象检测的方法和系统，更具体地，涉及根据优化的时钟周期矩阵对对象特征集进行大规模并行计算的方法和系统。该方法使用相关器阵列来计算待分析图像的每个部分的块和。执行贪婪启发式调度算法以产生优化的时钟周期矩阵，使得使用相同块和的重叠特征不尝试同时访问块，从而避免竞态存储器条件。处理系统可以采用各种硬连线超大规模集成（VLSI）芯片，例如现场可编程门阵列（FPGA），数字信号处理器（DSP）和专用集成电路（ASIC）。

9. 发明授权

US08116575B1 System for anomaly detection using sub-space analysis 有权
标题翻译：使用子空间分析的异常检测系统
公开(公告)号：US08116575B1
公开(公告)日：2012-02-14
申请号：US12072697
申请日：2008-02-26
申请人： Payam Saisan , Yuri Owechko , Swarup Medasani
发明人： Payam Saisan , Yuri Owechko , Swarup Medasani
IPC分类号： G06K9/62 , G06K9/46 , G06T11/20 , G01N33/48 , A61B5/00
CPC分类号： A61B5/1077 , A61B5/1176 , G06K9/00288 , G06K9/6247 , G06K9/6284
摘要： Described is a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing. The system is configured to generate a subspace model for a normal class using training images. The normal class represents normal objects in a common class. The system receives a novel image having an object in the common class. A set of geometric landmarks are identified in the object in the novel image for use in registering the image. The novel image is registered by warping the image so that the geometric landmarks coincide in the novel image and the training images, resulting in a warped novel image having an object. Thereafter, the system determines if the object in the warped novel image is anomalous by measuring the distance of the warped novel image from the subspace model. Finally, if anomalous, an operator is notified accordingly.
摘要翻译：描述了用于异常检测的系统，用于检测图像中的异常物体，例如人的衣服下方的隐藏物体。该系统被配置为使用训练图像为正常类生成子空间模型。普通类表示普通类中的普通对象。该系统接收具有公共类中的对象的新颖图像。在新颖图像中的对象中识别一组几何地标，以用于注册图像。通过使图像变形来记录新颖图像，使得新颖图像和训练图像中的几何标记重合，导致具有物体的翘曲的新颖图像。此后，系统通过测量翘曲的新颖图像与子空间模型的距离来确定翘曲的新颖图像中的对象是否是异常的。最后，如果异常，则相应地通知操作员。

10. 发明授权

US07636700B2 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：US07636700B2
公开(公告)日：2009-12-22
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： G06F17/00 , G06N5/02 , G06K9/00 , G06K9/34 , 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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