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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
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。
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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
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。
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公开(公告)号：US07715591B2
公开(公告)日：2010-05-11
申请号：US10132875
申请日：2002-04-24
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。
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公开(公告)号：US20060129843A1
公开(公告)日：2006-06-15
申请号：US11198798
申请日：2005-08-05
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.
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5.发明授权Method and apparatus for three-dimensional shape estimation using constrained disparity propagation 有权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
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。
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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
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。
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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
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。
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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。
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公开(公告)号：US09361523B1
公开(公告)日：2016-06-07
申请号：US12841078
申请日：2010-07-21
申请人： Yang Chen , Swarup Medasani , Qin Jiang , David L. Allen , Tsai-Ching Lu
发明人： Yang Chen , Swarup Medasani , Qin Jiang , David L. Allen , Tsai-Ching Lu
CPC分类号： G06K9/00758 , G06K9/00711 , G06K9/00718 , G06K9/00744 , G06K9/4676 , G06T2207/10016 , G06T2210/00
摘要： A method and system for video-content based retrieval is described. A query video depicting an activity is processed using interest point selection to find locations in the video that are relevant to that activity. A set of spatio-temporal descriptors such as self-similarity and 3-D SIFT are calculated within a local neighborhood of the set of interest points. An indexed video database containing videos similar to the query video is searched using the set of descriptors to obtain a set of candidate videos. The videos in the video database are indexed hierarchically using a vocabulary tree or other hierarchical indexing mechanism.
摘要翻译： 描述了基于视频内容的检索的方法和系统。 使用兴趣点选择来处理描绘活动的查询视频，以查找视频中与该活动相关的位置。 在一组感兴趣点的本地邻域内计算一组空间 - 时间描述符，例如自相似性和3-D SIFT。 使用该组描述符搜索包含与查询视频类似的视频的索引视频数据库，以获得一组候选视频。 视频数据库中的视频使用词汇树或其他分层索引机制分层索引。
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公开(公告)号：US08270671B1
公开(公告)日：2012-09-18
申请号：US12380415
申请日：2009-02-27
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）。
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