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1. 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。

2. 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。

3. 发明申请

US20120243741A1 Object Recognition For Security Screening and Long Range Video Surveillance 有权
标题翻译：对象识别用于安全检查和远程视频监控
公开(公告)号：US20120243741A1
公开(公告)日：2012-09-27
申请号：US13397924
申请日：2012-02-16
申请人： Vinay Damodar Shet , Claus Bahlmann , Maneesh Kumar Singh
发明人： Vinay Damodar Shet , Claus Bahlmann , Maneesh Kumar Singh
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/6296 , G06K2209/09
摘要： A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.
摘要翻译：一种检测被认为是威胁的图像数据中的对象的方法包括：注释至少一个训练图像的部分，以指示每个部分是否是对象的组件，使用多个第一顺序对描述对象的模式语法进行编码基于逻辑的谓词规则，训练不同的分量检测器，以基于所标注的训练图像来识别相应的一个分量，使用分量检测器处理图像数据以识别组件中的至少一个，以及执行规则以检测基于对象对所识别的组件。

4. 发明授权

US08903128B2 Object recognition for security screening and long range video surveillance 有权
标题翻译：安全检查和远程视频监控的对象识别
公开(公告)号：US08903128B2
公开(公告)日：2014-12-02
申请号：US13397924
申请日：2012-02-16
申请人： Vinay Damodar Shet , Claus Bahlmann , Maneesh Kumar Singh
发明人： Vinay Damodar Shet , Claus Bahlmann , Maneesh Kumar Singh
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/6296 , G06K2209/09
摘要： A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.
摘要翻译：一种检测被认为是威胁的图像数据中的对象的方法包括：注释至少一个训练图像的部分，以指示每个部分是否是对象的组件，使用多个第一顺序对描述对象的模式语法进行编码基于逻辑的谓词规则，训练不同的分量检测器，以基于所标注的训练图像来识别相应的一个分量，使用分量检测器处理图像数据以识别组件中的至少一个，以及执行规则以检测基于对象对所识别的组件。

5. 发明授权

US07466841B2 Method for traffic sign detection 有权
标题翻译：交通标志检测方法
公开(公告)号：US07466841B2
公开(公告)日：2008-12-16
申请号：US11109106
申请日：2005-04-19
申请人： Claus Bahlmann , Ying Zhu , Visvanathan Ramesh , Martin Pellkofer , Thorsten Köhler
发明人： Claus Bahlmann , Ying Zhu , Visvanathan Ramesh , Martin Pellkofer , Thorsten Köhler
IPC分类号： G06K9/00
CPC分类号： G06K9/00818 , G06K9/4633 , G06K9/4652
摘要： A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.
摘要翻译：公开了一种用于检测和识别至少一个交通标志的方法。接收具有多个图像帧的视频序列。使用一个或多个过滤器来测量指示感兴趣对象的至少一个图像帧中的特征。测量的特征被组合并聚合成表示对象可能存在的分数。分数融合在多个图像帧上以进行鲁棒检测。如果分数表示在图像帧的区域中可能存在对象，则该区域与模型对齐。然后确定该区域是否指示交通标志。如果该区域指示交通标志，该区域被分类为特定类型的交通标志。本发明还涉及训练用于检测和识别交通标志的系统。

6. 发明申请

US20060034484A1 Method for traffic sign detection 有权
标题翻译：交通标志检测方法
公开(公告)号：US20060034484A1
公开(公告)日：2006-02-16
申请号：US11109106
申请日：2005-04-19
申请人： Claus Bahlmann , Ying Zhu , Visvanathan Ramesh , Martin Pellkofer , Thorsten Kohler
发明人： Claus Bahlmann , Ying Zhu , Visvanathan Ramesh , Martin Pellkofer , Thorsten Kohler
IPC分类号： G06K9/00
CPC分类号： G06K9/00818 , G06K9/4633 , G06K9/4652
摘要： A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.
摘要翻译：公开了一种用于检测和识别至少一个交通标志的方法。接收具有多个图像帧的视频序列。使用一个或多个过滤器来测量指示感兴趣对象的至少一个图像帧中的特征。测量的特征被组合并聚合成表示对象可能存在的分数。分数融合在多个图像帧上以进行鲁棒检测。如果分数表示在图像帧的区域中可能存在对象，则该区域与模型对齐。然后确定该区域是否指示交通标志。如果该区域指示交通标志，该区域被分类为特定类型的交通标志。本发明还涉及训练用于检测和识别交通标志的系统。

7. 发明授权

US07769228B2 Method for combining boosted classifiers for efficient multi-class object detection 有权
标题翻译：用于组合升级分类器以实现高效多类对象检测的方法
公开(公告)号：US07769228B2
公开(公告)日：2010-08-03
申请号：US11111640
申请日：2005-04-21
申请人： Claus Bahlmann , Ying Zhu , Dorin Comaniciu , Thorsten Köhler , Martin Pellkofer
发明人： Claus Bahlmann , Ying Zhu , Dorin Comaniciu , Thorsten Köhler , Martin Pellkofer
IPC分类号： G06K9/62
CPC分类号： G06K9/6256 , G06K9/6228
摘要： A method for training a system for detecting multi-class objects in an image or a video sequence is described. A common ensemble of weak classifiers for a set of object classes is identified. For each object class, a separate weighting scheme is adapted for the ensemble of weak classifiers. A method for detecting objects of multiple classes in an image or a video sequence is also disclosed. Each class is assigned a detector that is implemented by a weighted combination of weak classifiers such that all of the detectors are based on a common ensemble of weak classifiers. Then weights are individually set for each class.
摘要翻译：描述了一种用于训练用于检测图像或视频序列中的多类对象的系统的方法。识别一组对象类的弱分类器的常见集合。对于每个对象类，单独的加权方案适用于弱分类器的集合。还公开了一种用于检测图像或视频序列中的多个类别的对象的方法。每个类被分配一个由弱分类器的加权组合实现的检测器，使得所有检测器都基于弱分类器的共同集合。然后，每个课程都可以单独设置权重。

8. 发明申请

US20070118492A1 Variational sparse kernel machines 审中-公开
标题翻译：变异稀疏内核机器
公开(公告)号：US20070118492A1
公开(公告)日：2007-05-24
申请号：US11599563
申请日：2006-11-14
申请人： Claus Bahlmann , Paolo Favaro
发明人： Claus Bahlmann , Paolo Favaro
IPC分类号： G06F15/18
CPC分类号： G06K9/6287 , G06K9/6278 , G06N20/00
摘要： A computer-implemented method for supervised learning for classification that unifies generative and discriminative methods in a variational framework includes providing training data for determining a classifier, defining a cost functional based on a kernel density, finding a function of the cost functional by searching for a zero crossing of joint probabilities for a label for a given data point, optimizing the cost functional using a gradient descent, and outputting the classifier comprising an optimized cost functional for classifying data.
摘要翻译：一种用于分类的监督学习的计算机实现方法，其在变分框架中统一生成和区分性方法，包括提供用于确定分类器的训练数据，基于核密度定义成本函数，通过搜索成功函数找到成本函数的函数用于给定数据点的标签的联合概率的零交叉，使用梯度下降优化成本函数，以及输出分类器，其包括用于对数据进行分类的优化成本函数。

9. 发明授权

US07764819B2 System and method for local pulmonary structure classification for computer-aided nodule detection 失效
标题翻译：用于计算机辅助结节检测的局部肺结构分类系统和方法
公开(公告)号：US07764819B2
公开(公告)日：2010-07-27
申请号：US11654963
申请日：2007-01-18
申请人： Claus Bahlmann , Xianlin Li , Kazunori Okada
发明人： Claus Bahlmann , Xianlin Li , Kazunori Okada
IPC分类号： G06K9/00
CPC分类号： G06K9/00 , A61B2090/364 , G06K9/6215 , G06K9/6226 , G06K2209/05
摘要： A method for classifying pulmonary structures in digitized images includes providing approximate target structure locations of one or more target structures in a digitized 3-dimensional (3D) image, fitting an anisotropic Gaussian model about said approximate target locations to generate more precise 3D target models and center locations of said one or more target structures, warping each said 3D target model into a 3D sphere, constructing a bounding manifold about each said warped 3D sphere, and identifying clusters on said bounding manifold wherein said one or more target structures are classified.
摘要翻译：在数字化图像中分类肺结构的方法包括在数字化的三维（3D）图像中提供一个或多个目标结构的近似目标结构位置，拟合关于所述近似目标位置的各向异性高斯模型以产生更精确的3D目标模型，以及所述一个或多个目标结构的中心位置将每个所述3D目标模型扭曲成3D球体，构成关于每个所述翘曲3D球体的边界歧管，以及识别所述边界歧管上的簇，其中所述一个或多个目标结构被分类。

10. 发明申请

US20100188288A1 MULTI-MODAL SPEED LIMIT ASSISTANT 有权
标题翻译：多模式速度限制助手
公开(公告)号：US20100188288A1
公开(公告)日：2010-07-29
申请号：US12246726
申请日：2008-10-07
申请人： Claus Bahlmann , Martin Pellkofer , Jan Giebel , Gregory Baratoff
发明人： Claus Bahlmann , Martin Pellkofer , Jan Giebel , Gregory Baratoff
IPC分类号： G01S19/40 , G06K9/00 , G06F17/30
CPC分类号： G08G1/0962 , B60W30/146 , B60W2420/42 , B60W2550/22 , B60W2550/402 , G01S19/14 , G06K9/00818
摘要： A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.
摘要翻译：速度限制助手（SLA）系统包括基于摄像头的SLA，基于地图的SLA和融合单元。基于相机的SLA被配置为确定输入图像的第一组概率，其中概率指示图像包括离散的一组限速标志的可能性。基于地图的SLA被配置为确定用于输入坐标的第二组概率，其中概率指示坐标对于离散速度限制集合之一的可能性。融合单元被配置为在第一和第二组概率上执行贝叶斯融合，以从离散的速度限制集合确定最终速度限制。

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