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1. 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。

2. 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。

3. 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.

4. 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。

5. 发明申请

US20050169529A1 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：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。

6. 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。

7. 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。

8. 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。

9. 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。

10. 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。

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