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。

公开(公告)号：US09449259B1

公开(公告)日：2016-09-20

申请号：US13558298

申请日：2012-07-25

申请人： Shinko Y. Cheng ,  Yuri Owechko ,  Swarup Medasani

发明人： Shinko Y. Cheng ,  Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/62 ,  G06K9/46 ,  G06K9/66 ,  G06K9/68 ,  G06K9/70 ,  G06N99/00

CPC分类号： G06K9/6257 ,  G06K9/3241 ,  G06K9/6217 ,  G06N99/005

摘要： The present invention relates to a classifier cascade object detection system. The system operates by inputting an image patch into parallel feature generation modules, each of the feature generation modules operable for extracting features from the image patch. The features are provided to an opportunistic classifier cascade, the opportunistic classifier cascade having a series of classifier stages. The opportunistic classifier cascade is executed by progressively evaluating, in each classifier in the classifier cascade, the features to produce a response, with each response progressively utilized by a decision function to generate a stage response for each classifier stage. If each stage response exceeds a stage threshold then the image patch is classified as a target object, and if the stage response from any of the decision functions does not exceed the stage threshold, then the image patch is classified as a non-target object.

摘要翻译： 本发明涉及分级器级联物体检测系统。 该系统通过将图像补丁输入到并行特征生成模块中来操作，每个特征生成模块可操作用于从图像补片提取特征。 这些特征被提供给机会分类器级联，机会分类器级联具有一系列分类器级。 机会分类器级联是通过在分类器级联中的每个分类器中逐步评估产生响应的特征来执行的，每个响应由决策函数逐渐被利用以产生每个分类器阶段的阶段响应。 如果每个阶段响应超过阶段阈值，则图像补丁被分类为目标对象，并且如果来自任何决策函数的阶段响应不超过阶段阈值，则将图像补丁分类为非目标对象。

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。

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。

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。

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。

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。

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。

公开(公告)号：US07558762B2

公开(公告)日：2009-07-07

申请号：US11385983

申请日：2006-03-20

申请人： Yuri Owechko ,  Swarup Medasani ,  Payam Saisan

发明人： Yuri Owechko ,  Swarup Medasani ,  Payam Saisan

IPC分类号： G06E1/00 ,  G06E3/00 ,  G06G7/00

CPC分类号： G06K9/6229 ,  G06K9/00369 ,  G06K9/6292

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 in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.

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的多个软件代理，用于对从多个视点看到的域中的对象进行分类。 每个代理是一个完整的分类器，并分配一个初始速度向量来探索对象解决方案的解空间。 每个代理被配置为执行迭代，迭代是针对潜在解决方案空间的解决方案空间中的搜索，其中每个代理跟踪其在与所观察到的最佳解（pbest）相关联的多维空间中的坐标，代理 已经确定了全球最佳解决方案（gbest），其中gbest用于存储所有代理商中的最佳位置。 每个速度向量向pbest和gbest变化，允许合作群集集中在对象附近并对对象进行分类。

公开(公告)号：US20070019865A1

公开(公告)日：2007-01-25

申请号：US11367755

申请日：2006-03-04

申请人： Yuri Owechko ,  Swarup Medasani

发明人： Yuri Owechko ,  Swarup Medasani

IPC分类号： G06K9/62

CPC分类号： G06K9/6229 ,  G06K9/00369 ,  G06K9/3233

摘要： An object recognition system is described that incorporates swarming classifiers with attention mechanisms. The object recognition system includes a cognitive map having a one-to-one relationship with an input image domain. The cognitive map records information that software agents utilize to focus a cooperative swarm's attention on regions likely to contain objects of interest. Multiple agents operate as a cooperative swarm to classify an object in the domain. Each agent is a classifier and is assigned a velocity vector to explore a solution space for object solutions. Each agent records its coordinates in multi-dimensional space that are an observed best solution that the agent has identified, and a global best solution that is used to store the best location among all agents. Each velocity vector thereafter changes to allow the swarm to concentrate on the vicinity of the object and classify the object when a classification level exceeds a preset threshold.

摘要翻译： 描述了包含具有注意机制的群集分类器的对象识别系统。 对象识别系统包括与输入图像域具有一对一关系的认知图。 认知地图记录软件代理人利用的信息，将合作群体的注意力集中在可能包含感兴趣对象的地区。 多个代理作为协作群来运行以对域中的对象进行分类。 每个代理是一个分类器，并分配一个速度向量来探索对象解决方案的解空间。 每个代理将其坐标记录在多维空间中，这是代理已经识别的最佳解决方案，也是用于在所有代理中存储最佳位置的全局最佳解决方案。 此后，每个速度矢量改变以允许群集集中在对象附近，并且当分类级别超过预设阈值时对对象进行分类。