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21. 发明授权

US07599894B2 Object recognition using a cognitive swarm vision framework with attention mechanisms 有权
标题翻译：使用认知群体视觉框架的对象识别与注意机制
公开(公告)号：US07599894B2
公开(公告)日：2009-10-06
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： G06F15/18 , G06K9/00 , 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。

22. 发明授权

US07561732B1 Method and apparatus for three-dimensional shape estimation using constrained disparity propagation 有权
标题翻译：使用约束差异传播的三维形状估计的方法和装置
公开(公告)号：US07561732B1
公开(公告)日：2009-07-14
申请号：US11051592
申请日：2005-02-04
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： G06T7/593 , G06T2207/10012 , G06T2207/20076
摘要： A method, an apparatus, and a computer program product for three-dimensional shape estimation using constrained disparity propagation are presented. An act of receiving a stereoscopic pair of images of an area occupied by at least one object is performed. Next, pattern regions and non-pattern regions are detected in the images. An initial estimate of śpatial disparities between the pattern regions in the images is generated. The initial estimate is used to generate a subsequent estimate of the spatial disparities between the non-pattern regions. The subsequent estimate is used to generate further subsequent estimates of the spatial disparities using the disparity constraints until there is no change between the results of subsequent iterations, generating a final estimate of the spatial disparities. A disparity map of the area occupied by at least one object is generated from the final estimate of the three-dimensional shape.
摘要翻译：提出了一种使用受限视差传播进行三维形状估计的方法，装置和计算机程序产品。执行接收由至少一个对象占据的区域的立体图像对的动作。接下来，在图像中检测图案区域和非图案区域。生成图像中的图案区域之间的空间差异的初始估计。初始估计用于产生非图案区域之间的空间差异的随后估计。随后的估计用于使用差异约束来生成空间差异的进一步后续估计，直到后续迭代的结果之间没有变化，产生空间差异的最终估计。从三维形状的最终估计中生成由至少一个对象占据的区域的视差图。

23. 发明申请

US20070183669A1 Multi-view cognitive swarm for object recognition and 3D tracking 有权
标题翻译：用于对象识别和3D跟踪的多视角认知群
公开(公告)号：US20070183669A1
公开(公告)日：2007-08-09
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： G06K9/62 , G06K9/46
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。

24. 发明授权

US09449259B1 Opportunistic cascade and cascade training, evaluation, and execution for vision-based object detection 有权
标题翻译：机会级联和级联培训，评估和执行基于视觉的对象检测
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。

25. 发明授权

US08645294B1 Method for image registration utilizing particle swarm optimization 有权
标题翻译：使用粒子群优化的图像配准方法
公开(公告)号：US08645294B1
公开(公告)日：2014-02-04
申请号：US12583238
申请日：2009-08-17
申请人： Yuri Owechko , Yang Chen , Swarup Medasani
发明人： Yuri Owechko , Yang Chen , Swarup Medasani
IPC分类号： G06F15/18
CPC分类号： G06N5/043 , G06K9/6229 , G06N3/006 , G06T7/337 , G06T7/35
摘要： Described is a method for image registration utilizing particle swarm optimization (PSO). In order to register two images, a set of image windows is first selected from a test image and transformed. A plurality of software agents is configured to operate as a cooperative swarm to optimize an objective function, and an objective function is then evaluated at the location of each agent. The objective function represents a measure of the difference or registration quality between at least one transformed image window and a reference image. The position vectors representing the current individual best solution found and the current global best solution found by all agents are then updated according to PSO dynamics. Finally, the current global best solution is compared with a maximum pixel value which signifies a match between an image window and the reference image. A system and a computer program product are also described.
摘要翻译：描述了使用粒子群优化（PSO）的图像配准的方法。为了注册两个图像，首先从测试图像中选择一组图像窗口并进行变换。多个软件代理被配置为作为协作群来操作以优化目标函数，然后在每个代理的位置处评估目标函数。目标函数表示至少一个变换的图像窗口和参考图像之间的差异或注册质量的度量。然后根据PSO动态更新表示当前找到的最佳解决方案的位置向量和所有代理发现的当前全局最佳解。最后，将当前全局最佳解决方案与表示图像窗口和参考图像之间的匹配的最大像素值进行比较。还描述了系统和计算机程序产品。

26. 发明授权

US08553989B1 Three-dimensional (3D) object recognition system using region of interest geometric features 有权
标题翻译：三维（3D）对象识别系统使用感兴趣区域的几何特征
公开(公告)号：US08553989B1
公开(公告)日：2013-10-08
申请号：US12799618
申请日：2010-04-27
申请人： Yuri Owechko , Swarup Medasani , Jim Nelson
发明人： Yuri Owechko , Swarup Medasani , Jim Nelson
IPC分类号： G06K9/00
CPC分类号： G06K9/00201 , G06K9/3233 , G06K9/468
摘要： The present invention relates to a method for three-dimensional (3D) object recognition using region of interest geometric features. The method includes acts of receiving an implicit geometry representation regarding a three-dimensional (3D) object of interest. A region of interest (ROI) is centered on the implicit geometry representation such that there is at least one intersection area between the ROI and the implicit geometry representation. Object shape features are calculated that reflect a location of the ROI with respect to the implicit geometry representation. The object shape features are assembled into a feature vector. A classification confidence value is generated with respect to a particular object classification. Finally, the 3D object of interest is classified as a particular object upon the output of a statistical classifier reaching a predetermined threshold.
摘要翻译：本发明涉及使用感兴趣区域几何特征的三维（3D）物体识别方法。该方法包括接收关于感兴趣的三维（3D）对象的隐式几何表示的动作。感兴趣区域（ROI）以隐式几何表示为中心，使得ROI和隐式几何表示之间存在至少一个交叉区域。计算反映相对于隐式几何表示的ROI的位置的对象形状特征。对象形状特征被组合成特征向量。相对于特定对象分类产生分类置信度值。最后，感兴趣的3D对象在统计分类器的输出达到预定阈值时被分类为特定对象。

27. 发明授权

US08406522B1 Method for flexible feature recognition in visual systems incorporating evolutionary optimization 有权
标题翻译：包含进化优化的视觉系统中的灵活特征识别方法
公开(公告)号：US08406522B1
公开(公告)日：2013-03-26
申请号：US12583239
申请日：2009-08-17
申请人： Yuri Owechko , Swarup Medasani
发明人： Yuri Owechko , Swarup Medasani
IPC分类号： G06K9/00
CPC分类号： G06K9/6228 , G06K9/6229
摘要： Described is a method for flexible feature adaptation and matching for object recognition in visual systems which incorporates evolutionary optimization. In the present invention, an analysis window is provided to select a portion of an input image to be analyzed for the presence or absence of an object. The analysis window is then divided into spatial regions, and a feature kernel function for each spatial region is selected and optimized. A feature value for each spatial region is calculated by finding a suitable location that generates the best matching features to a stored set using an optimization algorithm. The feature values are concatenated for the spatial regions to comprise a feature vector. Finally, the feature vector is processed by a classification algorithm, and a determination is made whether the object is present in the analysis window.
摘要翻译：描述了一种用于可视化系统中的物体识别的灵活特征适应和匹配的方法，其包括进化优化。在本发明中，提供了一个分析窗口，用于选择要分析的输入图像的一部分是否存在对象。然后将分析窗口分为空间区域，并选择并优化每个空间区域的特征核函数。通过使用优化算法找到对存储集合生成最佳匹配特征的合适位置来计算每个空间区域的特征值。特征值被连接以使空间区域包括特征向量。最后，通过分类算法处理特征向量，并确定对象是否存在于分析窗口中。

28. 发明授权

US08253792B2 Vision system for monitoring humans in dynamic environments 有权
标题翻译：用于在动态环境中监测人的视觉系统
公开(公告)号：US08253792B2
公开(公告)日：2012-08-28
申请号：US12549425
申请日：2009-08-28
申请人： James W. Wells , Roland J. Menassa , Charles W. Wampler, II , Swarup Medasani , Yuri Owechko , Kyungnam Kim , Yang Chen
发明人： James W. Wells , Roland J. Menassa , Charles W. Wampler, II , Swarup Medasani , Yuri Owechko , Kyungnam Kim , Yang Chen
IPC分类号： H04N9/47
CPC分类号： H04N7/181
摘要： A safety monitoring system for a workspace area. The workspace area related to a region having automated moveable equipment. A plurality of vision-based imaging devices capturing time-synchronized image data of the workspace area. Each vision-based imaging device repeatedly capturing a time synchronized image of the workspace area from a respective viewpoint that is substantially different from the other respective vision-based imaging devices. A visual processing unit for analyzing the time-synchronized image data. The visual processing unit processes the captured image data for identifying a human from a non-human object within the workspace area. The visual processing unit further determining potential interactions between a human and the automated moveable equipment. The visual processing unit further generating control signals for enabling dynamic reconfiguration of the automated moveable equipment based on the potential interactions between the human and the automated moveable equipment in the workspace area.
摘要翻译：用于工作区的安全监控系统。与具有自动移动设备的区域相关的工作空间区域。多个基于视觉的成像设备捕获工作区域的时间同步图像数据。每个基于视觉的成像设备从与其他各自的基于视觉的成像设备基本上不同的相应视点重复地捕获工作区域的时间同步图像。一种用于分析时间同步图像数据的可视处理单元。视觉处理单元从工作区域内的非人物对象处理用于识别人的拍摄图像数据。视觉处理单元进一步确定人与自动移动设备之间的潜在交互作用。视觉处理单元还基于人与工作空间区域中的自动移动设备之间的潜在交互，进一步产生用于实现自动移动设备的动态重新配置的控制信号。

29. 发明授权

US07778466B1 System and method for processing imagery using optical flow histograms 有权
标题翻译：使用光流直方图处理图像的系统和方法
公开(公告)号：US07778466B1
公开(公告)日：2010-08-17
申请号：US11004501
申请日：2004-12-02
申请人： Swarup Medasani , Yuri Owechko
发明人： Swarup Medasani , Yuri Owechko
IPC分类号： G06K9/48
CPC分类号： G06K9/4647 , G06K9/00744 , G06K9/00805
摘要： A method, computer program product, and system for processing imagery is presented. The imagery is processed by receiving data regarding a scene (such as from a sensor monitoring a scene). The scene includes an object having a dimension. Flow vectors are computed from the data, while a flow histogram space is generated from the flow vectors. A line segment (with a length) is found within the flow histogram space. An object in the scene is associated with the length segment, and the dimensions of the object are estimated based on the length of the line segment.
摘要翻译：提出了一种处理图像的方法，计算机程序产品和系统。通过接收关于场景的数据（例如从监视场景的传感器）处理图像。场景包括具有维度的对象。从数据计算流向量，而从流向量生成流直方图空间。在流直方图空间内找到一个线段（长度）。场景中的对象与长度段相关联，并且基于线段的长度来估计对象的尺寸。

30. 发明申请

US20070065014A1 Method for warped image object recognition 失效
标题翻译：扭曲图像对象识别方法
公开(公告)号：US20070065014A1
公开(公告)日：2007-03-22
申请号：US11231327
申请日：2005-09-20
申请人： Yuri Owechko , Swarup Medasani
发明人： Yuri Owechko , Swarup Medasani
IPC分类号： G06K9/46 , G06K9/32
CPC分类号： G06K9/46 , G06K9/42
摘要： A computer vision system includes distorting optics producing distorted or warped input images. The system includes an integral feature object classifier trained using an undistorted image space. Undistorted integral feature values are calculated directly from distorted input images without undistorting or dewarping the distorted input image.
摘要翻译：计算机视觉系统包括产生失真或翘曲输入图像的变形光学器件。该系统包括使用未失真的图像空间训练的整体特征对象分类器。非失真积分特征值直接从失真的输入图像计算，而不会使失真输入图像失真或杜绝。

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