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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. 发明授权

US06801662B1 Sensor fusion architecture for vision-based occupant detection 失效
标题翻译：用于基于视觉的乘员检测的传感器融合架构
公开(公告)号：US06801662B1
公开(公告)日：2004-10-05
申请号：US09685235
申请日：2000-10-10
申请人： Yuri Owechko , Narayan Srinivasa , Swarup S. Medasani , Riccardo Boscolo
发明人： Yuri Owechko , Narayan Srinivasa , Swarup S. Medasani , Riccardo Boscolo
IPC分类号： G06K962
CPC分类号： B60R21/01538 , B60R21/01542 , G06K9/00201 , G06K9/00362 , G06K9/6293
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. Determination of 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 from images captured by image sensors. These features are then processed by classification algorithms to produce occupant class confidences for various occupant types. The occupant class confidences are then fused and processed to determine the type of occupant. In a preferred embodiment, image features derived from image edges, motion, and range are used. Classification algorithms may be implemented by using trained C5 decision trees, trained Nonlinear Discriminant Analysis networks, Hausdorff template matching and trained Fuzzy Aggregate Networks. In an exemplary embodiment, class confidences are provided for a rear-facing infant seat, a front-facing infant seat, an adult out of position, and an adult in a normal or twisted position. Fusion of these class confidences derived from multiple image features increases the accuracy of the system and provides for correct determination of an airbag deployment decision.
摘要翻译：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，例如车辆内的乘客类型。然后可以确定乘客的类型，以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。然后通过分类算法对这些特征进行处理，以便为各种乘员类型产生乘员级别信心。然后对乘员班级信心进行融合和处理，以确定乘客的类型。在优选实施例中，使用从图像边缘，运动和范围导出的图像特征。分类算法可以通过使用经过训练的C5决策树，经过训练的非线性判别分析网络，Hausdorff模板匹配和经过训练的模糊聚合网络来实现。在一个示例性实施例中，提供了用于面向后方的婴儿座椅，前置婴儿座椅，成人不在位置以及处于正常或扭转位置的成年人的类别信号。从多个图像特征导出的这些类别信息的融合增加了系统的准确性，并提供了安全气囊部署决定的正确确定。

7. 发明申请

US20090055330A1 METHOD FOR ANOMALY PREDICTION OF BATTERY PARASITIC LOAD 失效
标题翻译：电池仿真负载异常预测方法
公开(公告)号：US20090055330A1
公开(公告)日：2009-02-26
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。

8. 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。

9. 发明授权

US06965900B2 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 失效
标题翻译：从电子搜索文件库电子提取的一组文件中选择的文件中电子提取应用程序特定多维信息的方法和装置
公开(公告)号：US06965900B2
公开(公告)日：2005-11-15
申请号：US10026065
申请日：2001-12-19
申请人： Narayan Srinivasa , Swarup S. Medasani , Yuri Owechko , Deepak Khosla
发明人： Narayan Srinivasa , Swarup S. Medasani , Yuri Owechko , Deepak Khosla
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30699 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： An apparatus and method provides application specific multidimensional information to an application running on a user computing device from a plurality of member documents electronically extracted from a library of electronically searchable documents. An information extractor is adapted to extract occurrences of prospective representations of dimensions of application specific multidimensional information and occurrences of non-application specific multidimensional information from the member documents. Also, an encoder is adapted to encode the occurrences of prospective dimensions of application specific and non-application specific multidimensional information contained in member documents. A member document identifier determines document formatting and decides whether to proceed with further processing. An information verification unit optionally verifies the extraction of application specific multidimensional information from the member documents. A database optionally stores and provides access to the application specific multidimensional information, which may for example be scheduled events having dimensions of time, location, identity.
摘要翻译：一种装置和方法从用户计算装置上运行的应用程序向从电子可搜索文件库电子提取的多个成员文件提供应用特定的多维信息。信息提取器适于从成员文档中提取应用程序特定多维信息的维度的预期表示以及非应用程序特定多维信息的出现。此外，编码器适于对包含在成员文档中的应用特定和非应用特定多维信息的预期维度的出现进行编码。成员文件标识符确定文档格式，并决定是否进行进一步处理。信息验证单元可选地验证从成员文档中提取特定于应用的多维信息。数据库可选地存储并提供对应用程序特定的多维信息的访问，其可以例如是具有时间，位置，身份的维度的调度事件。

10. 发明授权

US06456991B1 Classification method and apparatus based on boosting and pruning of multiple classifiers 失效
标题翻译：基于多分类器的增强和修剪的分类方法和装置
公开(公告)号：US06456991B1
公开(公告)日：2002-09-24
申请号：US09388858
申请日：1999-09-01
申请人： Narayan Srinivasa , Yuri Owechko
发明人： Narayan Srinivasa , Yuri Owechko
IPC分类号： G06N302
CPC分类号： G06K9/6222 , G06N3/0409 , G06N3/0454 , G06N3/082
摘要： A boosting and pruning system and method for utilizing a plurality of neural networks, preferably those based on adaptive resonance theory (ART), in order to increase pattern classification accuracy is presented. The method utilizes a plurality of N randomly ordered copies of the input data, which is passed to a plurality of sets of booster networks. Each of the plurality of N randomly ordered copies of the input data is divided into a plurality of portions, preferably with an equal allocation of the data corresponding to each class for which recognition is desired. The plurality of portions is used to train the set of booster networks. The rules generated by the set of booster networks are then pruned in an intra-booster pruning step, which uses a pair-wise Fuzzy AND operation to determine rule overlap and to eliminate rules which are sufficiently similar. This process results in a set of intra-booster pruned booster networks. A similar pruning process is applied in an inter-booster pruning process, which eliminates rules from the intra-booster pruned networks with sufficient overlap. The final, derivative booster network captures the essence of the plurality of sets of booster networks and provides for higher classification accuracy than available using a single network.
摘要翻译：提出了一种用于利用多个神经网络，优选基于自适应共振理论（ART）的神经网络的增强和修剪系统和方法，以便增加模式分类精度。该方法利用输入数据的多个N个随机排列的副本，其被传递到多组增强网络。输入数据的多个N个随机排列的副本中的每一个被分成多个部分，优选地具有与期望识别的每个类对应的数据的相等分配。多个部分用于训练该组增强网络。然后由增强器网络组生成的规则在促进器内修剪步骤中被修剪，其使用成对的模糊AND操作来确定规则重叠并消除足够相似的规则。该过程导致一组内部加速器修剪的增强网络。一个类似的修剪过程被应用在增强器之间的修剪过程中，该过程消除了具有足够重叠的内部加速器修剪的网络的规则。最终的衍生增强网络捕获多组增强网络的本质，并提供比使用单个网络可用的更高的分类精度。

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