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11. 发明授权

US08548231B2 Predicate logic based image grammars for complex visual pattern recognition 有权
标题翻译：用于复杂视觉模式识别的基于逻辑的图像语法
公开(公告)号：US08548231B2
公开(公告)日：2013-10-01
申请号：US12724954
申请日：2010-03-16
申请人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
发明人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
IPC分类号： G06K9/62 , G06F17/00
CPC分类号： G06K9/00369 , G06K9/626
摘要： First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.
摘要翻译：提供了第一级谓词逻辑，扩展了基于双精度的不确定性处理形式，作为正式编码模式语法的手段，解析一组图像特征，并检测在处理器上实现的不同兴趣模式的存在。来自不同来源的信息和来自检测的不确定性，被集成在双层框架内。计算机视觉领域中的自动逻辑规则权重学习应用规则权重优化方法，该方法将实例化推理树作为基于知识的神经网络进行聚合，以收敛于在边界框架内提供最佳性能的一组规则权重。应用是（a）检测部分闭塞下人的存在，（b）检测卫星图像中的大型复杂人造结构（c）检测视频中的时空人类和车辆活动，（c）解析图形用户界面。

12. 发明授权

US08060178B2 System and method for performing probabilistic classification and decision support using multidimensional medical image databases 有权
标题翻译：使用多维医学图像数据库执行概率分类和决策支持的系统和方法
公开(公告)号：US08060178B2
公开(公告)日：2011-11-15
申请号：US12243199
申请日：2008-10-01
申请人： Xiang Zhou , Dorin Comaniciu , Alok Gupta , Visvanathan Ramesh , Bhavani Duggirala , Diane Paine
发明人： Xiang Zhou , Dorin Comaniciu , Alok Gupta , Visvanathan Ramesh , Bhavani Duggirala , Diane Paine
IPC分类号： A61B5/00
CPC分类号： G06K9/6276 , A61B8/0883 , G06F19/00 , G06K9/6256 , G06K2209/05 , G06Q50/22 , G16H50/20 , Y10S128/92 , Y10S128/924
摘要： A system and method for providing decision support to a physician during a medical examination is disclosed. Data is received from a sensor representing a particular medical measurement. The received data includes image data. The received data and context data is analyzed with respect to one or more sets of training models. Probability values for the particular medical measurement and other measurements to be taken are derived based on the analysis and based on identified classes. The received image data is compared with training images. Distance values are determined between the received image data and the training images, and the training images are associated with the identified classes. Absolute value feature sensitivity scores are derived for the particular medical measurement and other measurements to be taken based on the analysis. The probability values, distance values and absolute value feature sensitivity scores are outputted to the user.
摘要翻译：公开了一种用于在医学检查期间向医师提供决策支持的系统和方法。从代表特定医疗测量的传感器接收数据。所接收的数据包括图像数据。相对于一组或多组训练模型分析接收到的数据和上下文数据。特定医疗测量和其他测量的概率值基于分析并基于识别的类别导出。将接收到的图像数据与训练图像进行比较。在接收的图像数据和训练图像之间确定距离值，并且训练图像与所识别的类别相关联。基于分析，针对特定医疗测量和其他测量得出绝对值特征灵敏度得分。将概率值，距离值和绝对值特征灵敏度得分输出给用户。

13. 发明授权

US07899209B2 Statistical modeling and performance characterization of a real-time dual camera surveillance system 有权
标题翻译：实时双摄像监控系统的统计建模与性能表征
公开(公告)号：US07899209B2
公开(公告)日：2011-03-01
申请号：US11112930
申请日：2005-04-22
申请人： Michael Greiffenhagen , Visvanathan Ramesh , Dorin Comaniciu
发明人： Michael Greiffenhagen , Visvanathan Ramesh , Dorin Comaniciu
IPC分类号： G06K9/00 , H04N7/18
CPC分类号： H04N7/181 , G06T7/277 , G06T7/77 , G06T2207/30196 , G06T2207/30232 , G06T2207/30241 , G08B13/19604 , G08B13/19608 , G08B13/19628 , G08B13/19641 , G08B13/19643 , G08B13/19647 , H04N5/232
摘要： The present invention relates to a method for visually detecting and tracking an object through a space. The method chooses modules for a restricting a search function within the space to regions with a high probability of significant change, the search function operating on images supplied by a camera. The method also derives statistical models for errors, including quantifying an indexing step performed by an indexing module, and tuning system parameters. Further the method applies a likelihood model for candidate hypothesis evaluation and object parameters estimation for locating the object.
摘要翻译：本发明涉及一种用于通过空间目视检测和跟踪物体的方法。该方法选择用于将空间内的搜索功能限制在具有很大变化概率的区域的模块，该搜索功能对由相机提供的图像进行操作。该方法还导出错误的统计模型，包括量化由索引模块执行的索引步骤，以及调整系统参数。此外，该方法应用候选假设评估的可行性模型和用于定位对象的对象参数估计。

14. 发明申请

US20100278420A1 Predicate Logic based Image Grammars for Complex Visual Pattern Recognition 有权
标题翻译：基于逻辑逻辑的图像语法用于复杂视觉模式识别
公开(公告)号：US20100278420A1
公开(公告)日：2010-11-04
申请号：US12724954
申请日：2010-03-16
申请人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
发明人： Vinay Damodar Shet , Maneesh Kumar Singh , Claus Bahlmann , Visvanathan Ramesh , Stephen P. Masticola , Jan Neumann , Toufiq Parag , Michael A. Gall , Roberto Antonio Suarez
IPC分类号： G06K9/62 , G06K9/46
CPC分类号： G06K9/00369 , G06K9/626
摘要： First order predicate logics are provided, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grmmars, to parse a set of image features, and detect the presence of different patterns of interest implemented on a processor. Information from different sources and uncertainties from detections, are integrated within the bilattice framework. Automated logical rule weight learning in the computer vision domain applies a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, to converge upon a set of rule weights that give optimal performance within the bilattice framework. Applications are in (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures in satellite imagery (c) detection of spatio-temporal human and vehicular activities in video and (c) parsing of Graphical User Interfaces.
摘要翻译：提供了第一级谓词逻辑，扩展了基于二进制的不确定性处理形式，作为正式编码模式格式的手段，解析一组图像特征，以及检测在处理器上实现的不同感兴趣模式的存在。来自不同来源的信息和来自检测的不确定性，被集成在双层框架内。计算机视觉领域中的自动逻辑规则权重学习应用规则权重优化方法，该方法将实例化推理树作为基于知识的神经网络进行聚合，以收敛于在边界框架内提供最佳性能的一组规则权重。应用是（a）检测部分闭塞下人的存在，（b）检测卫星图像中的大型复杂人造结构（c）检测视频中时空人类和车辆的活动，（c）解析图形用户界面。

15. 发明申请

US20100007740A1 Statistical modeling and performance characterization of a real-time dual camera surveillance system 有权
标题翻译：实时双摄像监控系统的统计建模与性能表征
公开(公告)号：US20100007740A1
公开(公告)日：2010-01-14
申请号：US11112930
申请日：2005-04-22
申请人： Michael Greiffenhagen , Visvanathan Ramesh , Dorin Comaniciu
发明人： Michael Greiffenhagen , Visvanathan Ramesh , Dorin Comaniciu
IPC分类号： H04N5/225 , G06F17/18
CPC分类号： H04N7/181 , G06T7/277 , G06T7/77 , G06T2207/30196 , G06T2207/30232 , G06T2207/30241 , G08B13/19604 , G08B13/19608 , G08B13/19628 , G08B13/19641 , G08B13/19643 , G08B13/19647 , H04N5/232
摘要： The present invention relates to a method for visually detecting and tracking an object through a space. The method chooses modules for a restricting a search function within the space to regions with a high probability of significant change, the search function operating on images supplied by a camera. The method also derives statistical models for errors, including quantifying an indexing step performed by an indexing module, and tuning system parameters. Further the method applies a likelihood model for candidate hypothesis evaluation and object parameters estimation for locating the object.
摘要翻译：本发明涉及一种用于通过空间目视检测和跟踪物体的方法。该方法选择用于将空间内的搜索功能限制在具有很大变化概率的区域的模块，该搜索功能对由相机提供的图像进行操作。该方法还导出错误的统计模型，包括量化由索引模块执行的索引步骤，以及调整系统参数。此外，该方法应用候选假设评估的可行性模型和用于定位对象的对象参数估计。

16. 发明授权

US07620208B2 System and method for detecting features from images of vehicles 失效
标题翻译：用于从车辆图像检测特征的系统和方法
公开(公告)号：US07620208B2
公开(公告)日：2009-11-17
申请号：US11313599
申请日：2005-12-21
申请人： Ying Zhu , Binglong Xie , Visvanathan Ramesh , Martin Pellkofer , Thorsten Köhler
发明人： Ying Zhu , Binglong Xie , Visvanathan Ramesh , Martin Pellkofer , Thorsten Köhler
IPC分类号： G06K9/00 , H04N7/18
CPC分类号： G06K9/4609 , G06K2209/23
摘要： A method of detecting a feature of a vehicle in an image of a vehicle includes providing a digitized image of a vehicle, providing a first filter mask over a first subdomain of the image, where the filter mask is placed to detect a feature in the image, calculating a function of a gradient of the image inside the first masked subdomain, and detecting the presence or absence of a vehicle feature within the first masked subdomain based on the value of the gradient function.
摘要翻译：一种在车辆图像中检测车辆的特征的方法包括提供车辆的数字化图像，在图像的第一子域上提供第一滤波器掩模，其中放置滤波器掩码以检测图像中的特征计算第一掩蔽子域内的图像的梯度的函数，以及基于梯度函数的值来检测第一掩蔽子域内的车辆特征的存在或不存在。

17. 发明授权

US07139409B2 Real-time crowd density estimation from video 有权
公开(公告)号：US07139409B2
公开(公告)日：2006-11-21
申请号：US09944317
申请日：2001-08-31
申请人： Nikos Paragios , Visvanathan Ramesh , Bjoern Stenger , Frans Coetzee
发明人： Nikos Paragios , Visvanathan Ramesh , Bjoern Stenger , Frans Coetzee
IPC分类号： G06K9/00
CPC分类号： G06K9/00778 , G06K9/38 , G06K9/4638 , G06K9/4652 , G06T7/254
摘要： A system and method for automated and/or semi-automated analysis of video for discerning patterns of interest in video streams. In a preferred embodiment, the present invention is directed to identifying patterns of interest in indoor settings. In one aspect, the present invention deals with the change detection problem using a Markov Random Field approach where information from different sources are naturally combined with additional constraints to provide the final detection map. A slight modification is made of the regularity term within the MRF model that accounts for real-discontinuities in the observed data. The defined objective function is implemented in a multi-scale framework that decreases the computational cost and the risk of convergence to local minima. To achieve real-time performance, fast deterministic relaxation algorithms are used to perform the minimization. The crowdedness measure used is a geometric measure of occupancy that is quasi-invariant to objects translating on the platform.

18. 发明申请

US20060188131A1 System and method for camera tracking and pose estimation 有权
标题翻译：摄像机跟踪和姿态估计的系统和方法
公开(公告)号：US20060188131A1
公开(公告)日：2006-08-24
申请号：US11359673
申请日：2006-02-22
申请人： Xiang Zhang , Yakup Genc , Visvanathan Ramesh , Yanghai Tsin
发明人： Xiang Zhang , Yakup Genc , Visvanathan Ramesh , Yanghai Tsin
IPC分类号： G06K9/00
CPC分类号： G06T7/246 , G06T7/74
摘要： A method of tracking a pose of a moving camera includes receiving a first image from a camera, receiving a sequence of digitized images from said camera, recording, for each of said sequence of digitized images, the pose and 2D correspondences of landmarks, reconstructing a location and appearance of a 2-dimensional texture patch from 2D correspondences of the landmarks by triangulation and optimization, computing back-projection errors by comparing said reconstructed texture patch with said first received image; and reconstructing said location and appearance of said 2-dimensional texture patch from the 2D correspondences of the landmarks of said sequence of digitized images by triangulation and optimization after eliminating those landmarks with large back-projection errors.
摘要翻译：跟踪移动摄像机的姿态的方法包括：从相机接收第一图像，从所述照相机接收数字化图像序列，针对所述数字化图像序列中的每一个记录地标的姿态和2D对应，重建通过三角测量和优化从地标的2D对应的二维纹理贴片的位置和外观，通过将所述重建纹理贴片与所述第一接收图像进行比较来计算反投影误差; 以及通过三角测量和优化，从所述数字化图像序列的地标的二维对应中重建所述二维纹理补片的所述位置和外观，并且消除了具有大的反投影误差的这些地标。

19. 发明授权

US07031523B2 Systems and methods for automatic scale selection in real-time imaging 有权
公开(公告)号：US07031523B2
公开(公告)日：2006-04-18
申请号：US10147092
申请日：2002-05-16
申请人： Dorin Comaniciu , Visvanathan Ramesh
发明人： Dorin Comaniciu , Visvanathan Ramesh
IPC分类号： G06K9/46 , G06K9/66
CPC分类号： G06K9/42 , G06K9/6226 , G06K9/6298 , G06K2209/01 , G06T7/13 , G06T7/90 , G06T2207/20016 , G06T2207/20048
摘要： A system and method for automatic scale selection in real-time image and video processing and computer vision applications. In one aspect, a non-parametric variable bandwidth mean shift technique, which is based on adaptive estimation of a normalized density gradient, is used for detecting one or more modes in the underlying data and clustering the underlying data. In another aspect, a data-driven bandwidth (or scale) selection technique is provided for the variable bandwidth mean shift method, which estimates for each data point the covariance matrix that is the most stable across a plurality of scales. The methods can be used for detecting modes and clustering data for various types of data such as image data, video data speech data, handwriting data, etc.

20. 发明授权

US07006128B2 Object detection for sudden illumination changes using order consistency 有权
公开(公告)号：US07006128B2
公开(公告)日：2006-02-28
申请号：US10158632
申请日：2002-05-30
申请人： Binglong Xie , Visvanathan Ramesh , Terrance Boult
发明人： Binglong Xie , Visvanathan Ramesh , Terrance Boult
IPC分类号： H04N7/18
CPC分类号： G08B13/19604 , G06T7/254
摘要： A system and method for object detection with sudden illumination changes provides a system including a camera model unit for computing a difference map between a first input image and a second input image, an illumination model unit in signal communication with the camera model unit for comparing the sign of the difference between two pixels in a first input image against the sign of the difference between two pixels in a second input image, and a CPU in signal communication with the camera model unit and the illumination model unit for detecting a changed object in accordance with said comparison when said sign of the difference is changed; and a corresponding method including providing a first image having a number of pixels, receiving a second image subsequent to the first image, calculating a difference map between the first and second images, selecting a pair of pixel locations from the first or second images in accordance with the difference map, statistically modeling a camera noise level for the first and second images, computing the likelihood of order inconsistency between a pair of pixel values at the selected locations in the first image and a pair of pixel values at the selected locations in the second image in accordance with the camera noise level; and detecting a changed object in accordance with the computed likelihood.

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