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1. 发明授权

US08116575B1 System for anomaly detection using sub-space analysis 有权
标题翻译：使用子空间分析的异常检测系统
公开(公告)号：US08116575B1
公开(公告)日：2012-02-14
申请号：US12072697
申请日：2008-02-26
申请人： Payam Saisan , Yuri Owechko , Swarup Medasani
发明人： Payam Saisan , Yuri Owechko , Swarup Medasani
IPC分类号： G06K9/62 , G06K9/46 , G06T11/20 , G01N33/48 , A61B5/00
CPC分类号： A61B5/1077 , A61B5/1176 , G06K9/00288 , G06K9/6247 , G06K9/6284
摘要： Described is a system for anomaly detection to detect an anomalous object in an image, such as a concealed object beneath a person's clothing. The system is configured to generate a subspace model for a normal class using training images. The normal class represents normal objects in a common class. The system receives a novel image having an object in the common class. A set of geometric landmarks are identified in the object in the novel image for use in registering the image. The novel image is registered by warping the image so that the geometric landmarks coincide in the novel image and the training images, resulting in a warped novel image having an object. Thereafter, the system determines if the object in the warped novel image is anomalous by measuring the distance of the warped novel image from the subspace model. Finally, if anomalous, an operator is notified accordingly.
摘要翻译：描述了用于异常检测的系统，用于检测图像中的异常物体，例如人的衣服下方的隐藏物体。该系统被配置为使用训练图像为正常类生成子空间模型。普通类表示普通类中的普通对象。该系统接收具有公共类中的对象的新颖图像。在新颖图像中的对象中识别一组几何地标，以用于注册图像。通过使图像变形来记录新颖图像，使得新颖图像和训练图像中的几何标记重合，导致具有物体的翘曲的新颖图像。此后，系统通过测量翘曲的新颖图像与子空间模型的距离来确定翘曲的新颖图像中的对象是否是异常的。最后，如果异常，则相应地通知操作员。

2. 发明授权

US07558762B2 Multi-view cognitive swarm for object recognition and 3D tracking 有权
标题翻译：用于对象识别和3D跟踪的多视角认知群
公开(公告)号：US07558762B2
公开(公告)日：2009-07-07
申请号：US11385983
申请日：2006-03-20
申请人： Yuri Owechko , Swarup Medasani , Payam Saisan
发明人： Yuri Owechko , Swarup Medasani , Payam Saisan
IPC分类号： G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G06K9/6229 , G06K9/00369 , G06K9/6292
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 in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.
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的多个软件代理，用于对从多个视点看到的域中的对象进行分类。每个代理是一个完整的分类器，并分配一个初始速度向量来探索对象解决方案的解空间。每个代理被配置为执行迭代，迭代是针对潜在解决方案空间的解决方案空间中的搜索，其中每个代理跟踪其在与所观察到的最佳解（pbest）相关联的多维空间中的坐标，代理已经确定了全球最佳解决方案（gbest），其中gbest用于存储所有代理商中的最佳位置。每个速度向量向pbest和gbest变化，允许合作群集集中在对象附近并对对象进行分类。

3. 发明授权

US09805301B1 Dynamic background estimation for video analysis using evolutionary optimization 有权
公开(公告)号：US09805301B1
公开(公告)日：2017-10-31
申请号：US12583519
申请日：2009-08-20
申请人： Yuri Owechko , Swarup Medasani , Payam Saisan
发明人： Yuri Owechko , Swarup Medasani , Payam Saisan
IPC分类号： G06N3/00 , G06K9/00
CPC分类号： G06N3/006 , G06K9/00335 , G06K9/00369 , G06K9/00624 , G06K9/00771 , G06K9/3233 , G06K9/6229
摘要： Described is a system for dynamic background estimation which utilizes Particle Swarm Optimization (PSO). The present invention comprises a system, method, and computer program product for accurate estimation of a background mask corresponding to a dynamically changing scene. The system is configured to construct a background template model of a scene, and then capture an image of a current view of the scene with a camera. Thereafter, the system generates an image-based template matching cost function as an optimization problem, where the objective is to identify and fit a corresponding subregion of the background template model to the current camera view. The cost function is optimized using a PSO search algorithm. Finally, the system is configured to generate the corresponding subregion of the background template model for display. The inherent efficiency of PSO makes this system conducive for use in applications requiring real-time background estimation.

4. 发明申请

US20070183669A1 Multi-view cognitive swarm for object recognition and 3D tracking 有权
标题翻译：用于对象识别和3D跟踪的多视角认知群
公开(公告)号：US20070183669A1
公开(公告)日：2007-08-09
申请号：US11385983
申请日：2006-03-20
申请人： Yuri Owechko , Swarup Medasani , Payam Saisan
发明人： Yuri Owechko , Swarup Medasani , Payam Saisan
IPC分类号： G06K9/62 , G06K9/46
CPC分类号： G06K9/6229 , G06K9/00369 , G06K9/6292
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 in a domain as seen from multiple view points. Each agent is a complete classifier and is assigned an initial velocity vector to explore a solution space for object solutions. Each agent is configured to perform an iteration, the iteration being a search in the solution space for a potential solution optima where each agent keeps track of its coordinates in multi-dimensional space that are associated with an observed best solution (pbest) that the agent has identified, and a global best solution (gbest) where the gbest is used to store the best location among all agents. Each velocity vector changes towards pbest and gbest, allowing the cooperative swarm to concentrate on the vicinity of the object and classify the object.
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的多个软件代理，用于对从多个视点看到的域中的对象进行分类。每个代理是一个完整的分类器，并分配一个初始速度向量来探索对象解决方案的解空间。每个代理被配置为执行迭代，迭代是针对潜在解决方案空间的解决方案空间中的搜索，其中每个代理跟踪其在与所观察到的最佳解（pbest）相关联的多维空间中的坐标，代理已经确定了全球最佳解决方案（gbest），其中gbest用于存储所有代理商中的最佳位置。每个速度向量向pbest和gbest变化，允许合作群集集中在对象附近并对对象进行分类。

5. 发明授权

US08335346B2 Identifying whether a candidate object is from an object class 有权
标题翻译：识别候选对象是否来自对象类
公开(公告)号：US08335346B2
公开(公告)日：2012-12-18
申请号：US12049830
申请日：2008-03-17
申请人： Payam Saisan , Shubha L. Kadambe
发明人： Payam Saisan , Shubha L. Kadambe
IPC分类号： G06K9/00
CPC分类号： G06K9/48 , G01S15/8902 , G06K9/6247
摘要： In one aspect, a method to identify a candidate object includes receiving an image of the candidate object and projecting the received image onto an image subspace. The image subspace is formed from images of known objects of a class. The method also includes determining whether the candidate object is in the object class based on the received image and the image subspace using a likelihood ratio. The likelihood ratio includes a first probability density indicating a probability an object is in the object class and a second probability density indicating a probability an object is not in the class. The first probability density and the second probability are each a function of a distance of the received image to the image subspace.
摘要翻译：一方面，一种识别候选对象的方法包括接收候选对象的图像并将接收的图像投影到图像子空间上。图像子空间由类的已知对象的图像形成。该方法还包括基于接收到的图像和使用似然比的图像子空间来确定候选对象是否在对象类中。似然比包括指示对象在对象类中的概率的第一概率密度和指示对象不在类中的概率的第二概率密度。第一概率密度和第二概率各自是接收到的图像与图像子空间的距离的函数。

6. 发明申请

US20090232406A1 REDUCING FALSE ALARMS IN IDENTIFYING WHETHER A CANDIDATE IMAGE IS FROM AN OBJECT CLASS 有权
标题翻译：减少伪代码，以确定候选图像来自对象类别
公开(公告)号：US20090232406A1
公开(公告)日：2009-09-17
申请号：US12049791
申请日：2008-03-17
申请人： Payam Saisan
发明人： Payam Saisan
IPC分类号： G06K9/62
CPC分类号： G06K9/6247 , G01S15/88 , G01S15/89
摘要： In one aspect, a method to reduce false alarms in identifying whether a candidate image is from an object class includes projecting the candidate image onto an object class subspace and projecting the candidate image onto a non-object class subspace. The method also includes determining whether the candidate image is from the object class using a Bayesian decision function based on the projections on the object class subspace and the non-object class subspace.In another aspect, a method to reduce false alarms in identifying whether a candidate mine image is from a mine class includes projecting the candidate mine image onto a mine subspace and projecting the candidate mine image onto a non-mine subspace. The method also includes determining whether the candidate mine image represents a mine using a Bayesian decision function based on the projections on the mine class subspace and the non-mine class subspace.
摘要翻译：一方面，在识别候选图像是否来自对象类时减少误报警的方法包括将候选图像投影到对象类子空间上，并将候选图像投影到非对象类子空间上。该方法还包括基于对对象类子空间和非对象类子空间的投影，使用贝叶斯判决函数来确定候选图像是否来自对象类。在另一方面，一种在识别候选矿井图像是否来自矿井类别时减少误报警的方法包括将候选矿井图像投影到矿井子空间上，并将候选矿井图像投影到非矿井子空间。该方法还包括基于对矿类子空间和非矿类子空间的预测，确定候选矿图像是否使用贝叶斯决策函数代表矿井。

7. 发明授权

US08655079B2 Reducing false alarms in identifying whether a candidate image is from an object class 有权
标题翻译：在识别候选图像是否来自对象类时减少虚假警报
公开(公告)号：US08655079B2
公开(公告)日：2014-02-18
申请号：US12049791
申请日：2008-03-17
申请人： Payam Saisan
发明人： Payam Saisan
IPC分类号： G06K9/46 , G06K9/00 , G06K9/62 , H04N5/225 , G01S13/00
CPC分类号： G06K9/6247 , G01S15/88 , G01S15/89
摘要： In one aspect, a method to reduce false alarms in identifying whether a candidate image is from an object class includes projecting the candidate image onto an object class subspace and projecting the candidate image onto a non-object class subspace. The method also includes determining whether the candidate image is from the object class using a Bayesian decision function based on the projections on the object class subspace and the non-object class subspace.In another aspect, a method to reduce false alarms in identifying whether a candidate mine image is from a mine class includes projecting the candidate mine image onto a mine subspace and projecting the candidate mine image onto a non-mine subspace. The method also includes determining whether the candidate mine image represents a mine using a Bayesian decision function based on the projections on the mine class subspace and the non-mine class subspace.
摘要翻译：一方面，在识别候选图像是否来自对象类时减少误报警的方法包括将候选图像投影到对象类子空间上，并将候选图像投影到非对象类子空间上。该方法还包括基于对对象类子空间和非对象类子空间的投影，使用贝叶斯判决函数来确定候选图像是否来自对象类。在另一方面，一种在识别候选矿井图像是否来自矿井类别时减少误报警的方法包括将候选矿井图像投影到矿井子空间上，并将候选矿井图像投影到非矿井子空间。该方法还包括基于对矿类子空间和非矿类子空间的预测，确定候选矿图像是否使用贝叶斯决策函数代表矿井。

8. 发明申请

US20090060353A1 IDENTIFYING WHETHER A CANDIDATE OBJECT IS FROM AN OBJECT CLASS 有权
标题翻译：识别候选对象来自对象类
公开(公告)号：US20090060353A1
公开(公告)日：2009-03-05
申请号：US12049830
申请日：2008-03-17
申请人： Payam Saisan , Shubha L. Kadambe
发明人： Payam Saisan , Shubha L. Kadambe
IPC分类号： G06K9/62
CPC分类号： G06K9/48 , G01S15/8902 , G06K9/6247
摘要： In one aspect, a method to identify a candidate object includes receiving an image of the candidate object and projecting the received image onto an image subspace. The image subspace is formed from images of known objects of a class. The method also includes determining whether the candidate object is in the object class based on the received image and the image subspace using a likelihood ratio. The likelihood ratio includes a first probability density indicating a probability an object is in the object class and a second probability density indicating a probability an object is not in the class. The first probability density and the second probability are each a function of a distance of the received image to the image subspace.
摘要翻译：一方面，一种识别候选对象的方法包括接收候选对象的图像并将接收的图像投影到图像子空间上。图像子空间由类的已知对象的图像形成。该方法还包括基于接收到的图像和使用似然比的图像子空间来确定候选对象是否在对象类中。似然比包括指示对象在对象类中的概率的第一概率密度和指示对象不在类中的概率的第二概率密度。第一概率密度和第二概率各自是接收到的图像与图像子空间的距离的函数。

9. 发明授权

US07881916B1 Precision shape sensing of elastically deformable materials 失效
标题翻译：可弹性变形材料的精密形状检测
公开(公告)号：US07881916B1
公开(公告)日：2011-02-01
申请号：US11986676
申请日：2007-11-21
申请人： Payam Saisan
发明人： Payam Saisan
IPC分类号： G06F17/50 , G06F19/00 , G06G7/48 , G05B13/02
CPC分类号： G01B5/20 , G01B21/20
摘要： The present invention provides a method and apparatus for sensing and determining the shapes of deformable materials, shaped by a plurality of actuators, using sparse sensor network topologies. The method comprises placing a plurality of curvature sensors on the surface of a deformable material to measure the local curvatures of the deformable material. The plurality of curvature sensors generate a collection of sensor data correlated to the local curvatures. Actuator parameters are generated from sensor data by assuming linear approximation between sensors data and actuator parameters. The shape of the deformable material is determined from the generated actuator parameters.
摘要翻译：本发明提供一种用于使用稀疏传感器网络拓扑来感测和确定由多个致动器成形的可变形材料的形状的方法和装置。该方法包括将多个曲率传感器放置在可变形材料的表面上以测量可变形材料的局部曲率。多个曲率传感器产生与局部曲率相关的传感器数据的集合。通过假设传感器数据和执行器参数之间的线性近似，从传感器数据生成执行器参数。可变形材料的形状由所产生的致动器参数确定。

10. 发明授权

US08437556B1 Shape-based object detection and localization system 有权
标题翻译：基于形状的物体检测和定位系统
公开(公告)号：US08437556B1
公开(公告)日：2013-05-07
申请号：US12589483
申请日：2009-10-21
申请人： Payam Saisan
发明人： Payam Saisan
IPC分类号： G06K9/62 , G06K9/00 , G06K9/48
CPC分类号： G06K9/00288 , G06K9/6247 , G06K9/6284 , G06K2209/09 , G06T7/0004 , G06T7/13 , G06T2207/10116 , G06T2207/30112
摘要： Described is a system for identifying a concealed object of interest. The system is initialized by receiving an image in grayscale. The image is processed image to identify feature rich regions, which are identified as a binary mask describing edge pixels and non-edge pixels. A set of edge pixels are then randomly sampled. An object detector function is then used on the set of edge pixels to determine if an object of interest is present in the set of edge pixels.
摘要翻译：描述了用于识别隐藏的感兴趣对象的系统。通过接收灰度图像来初始化系统。图像被处理图像以识别特征富集区域，其被识别为描述边缘像素和非边缘像素的二进制掩码。然后随机抽取一组边缘像素。然后在该组边缘像素上使用对象检测器功能来确定一组边缘像素中是否存在感兴趣对象。

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