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1.发明申请CLUSTERING NODES IN A SELF-ORGANIZING MAP USING AN ADAPTIVE RESONANCE THEORY NETWORK 有权使用自适应谐振理论网络在自组织地图中的聚类数公开(公告)号：US20110052067A1
公开(公告)日：2011-03-03
申请号：US12551154
申请日：2009-08-31
IPC分类号： G06K9/46
CPC分类号： G06K9/6222
摘要： Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.
摘要翻译： 公开了使用从图像数据提取的像素级微特征来发现对象类型簇的技术。 自组织图和自适应共振理论（SOM-ART）网络用于基于像素级微特征对图像数据中描绘的对象进行分类。 重要的是，对象类型簇的发现是无监督的，即独立于定义特定对象的任何训练数据执行，允许行为识别系统放弃训练阶段，并且对象分类进行而不受特定对象定义的约束。 SOM-ART网络是自适应的，能够在发现对象类型集群并对对象进行分类时学习。
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2.发明授权公开(公告)号：US08494222B2
公开(公告)日：2013-07-23
申请号：US13472214
申请日：2012-05-15
CPC分类号： G06K9/00771
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 可以使用自组织图（SOM）和自适应共振理论（ART）网络的组合来识别每个簇层处的各种不同的异常输入。 随着逐渐更高层的皮层模型组件代表逐渐提高的抽象水平，在较高级别的皮质模型中发生的异常表示对应于逐渐复杂的行为模式的行为异常的观察。
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公开(公告)号：US08270733B2
公开(公告)日：2012-09-18
申请号：US12551276
申请日：2009-08-31
申请人： Wesley Kenneth Cobb , David Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung Seow , Gang Xu
发明人： Wesley Kenneth Cobb , David Friedlander , Rajkiran Kumar Gottumukkal , Ming-Jung Seow , Gang Xu
CPC分类号： G06K9/6251 , G06K9/00771
摘要： Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
摘要翻译： 公开了用于在从图像数据提取的前景对象的分类期间识别异常对象类型的技术。 基于从图像数据提取的像素级微特征，使用自组织图和自适应共振理论（SOM-ART）网络来发现对象类型簇并对图像数据中描绘的对象进行分类。 重要的是，对象类型簇的发现是无监督的，即独立于定义特定对象的任何训练数据执行，允许行为识别系统放弃训练阶段，并且对象分类进行而不受特定对象定义的约束。 SOM-ART网络是自适应的，能够在发现对象类型集群并分类对象并识别异常对象类型时学习。
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4.发明申请公开(公告)号：US20110064267A1
公开(公告)日：2011-03-17
申请号：US12561956
申请日：2009-09-17
IPC分类号： G06K9/00
CPC分类号： G06K9/00771
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 可以使用自组织图（SOM）和自适应共振理论（ART）网络的组合来识别每个簇层处的各种不同的异常输入。 随着逐渐更高层的皮层模型组件代表逐渐提高的抽象水平，在较高级别的皮质模型中发生的异常表示对应于逐渐复杂的行为模式的行为异常的观察。
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公开(公告)号：US20110052068A1
公开(公告)日：2011-03-03
申请号：US12551276
申请日：2009-08-31
申请人： WESLEY KENNETH COBB , David FRIEDLANDER , RAJKIRAN KUMAR GOTTUMUKKAL , MING-JUNG SEOW , GANG XU
发明人： WESLEY KENNETH COBB , David FRIEDLANDER , RAJKIRAN KUMAR GOTTUMUKKAL , MING-JUNG SEOW , GANG XU
IPC分类号： G06K9/46
CPC分类号： G06K9/6251 , G06K9/00771
摘要： Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.
摘要翻译： 公开了用于在从图像数据提取的前景对象的分类期间识别异常对象类型的技术。 基于从图像数据提取的像素级微特征，使用自组织图和自适应共振理论（SOM-ART）网络来发现对象类型簇并对图像数据中描绘的对象进行分类。 重要的是，对象类型簇的发现是无监督的，即独立于定义特定对象的任何训练数据执行，允许行为识别系统放弃训练阶段，并且对象分类进行而不受特定对象定义的约束。 SOM-ART网络是自适应的，能够在发现对象类型集群并分类对象并识别异常对象类型时学习。
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6.发明申请公开(公告)号：US20120224746A1
公开(公告)日：2012-09-06
申请号：US13472214
申请日：2012-05-15
IPC分类号： G06K9/62
CPC分类号： G06K9/00771
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 可以使用自组织图（SOM）和自适应共振理论（ART）网络的组合来识别每个簇层处的各种不同的异常输入。 随着逐渐更高层的皮层模型组件代表逐渐提高的抽象水平，在较高级别的皮质模型中发生的异常表示对应于逐渐复杂的行为模式的行为异常的观察。
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公开(公告)号：US20130018395A1
公开(公告)日：2013-01-17
申请号：US13566901
申请日：2012-08-03
IPC分类号： A61B17/03
CPC分类号： A61F2/0063 , A61F2002/0072
摘要： An apparatus, method, and system for the deployment of surgical mesh material, which are particularly suited for use in the laparoscopic surgical repair of hernias. Any suitable surgical mesh can be placed between at least two elongate retaining members; wrapped around the elongate retaining members; inserted into a patient; and then deployed using at least two elongate deploying members on either side of the mesh.
摘要翻译： 一种用于部署外科网状材料的装置，方法和系统，其特别适用于腹腔镜手术修复疝气。 任何合适的手术网可以放置在至少两个细长的保持构件之间; 缠绕在细长的保持构件上; 插入患者; 然后使用网格的任一侧上的至少两个细长的展开构件展开。
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8.发明授权公开(公告)号：US08180105B2
公开(公告)日：2012-05-15
申请号：US12561956
申请日：2009-09-17
CPC分类号： G06K9/00771
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 可以使用自组织图（SOM）和自适应共振理论（ART）网络的组合来识别每个簇层处的各种不同的异常输入。 随着逐渐更高层的皮层模型组件代表逐渐提高的抽象水平，在较高级别的皮质模型中发生的异常表示对应于逐渐复杂的行为模式的行为异常的观察。
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9.发明授权Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing 有权视频监控系统配置为使用交替的聚类和排序层分析复杂行为公开(公告)号：US08170283B2
公开(公告)日：2012-05-01
申请号：US12561977
申请日：2009-09-17
CPC分类号： G06K9/6222 , G06K9/00335 , G06K9/00771 , G06K9/6284
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 视频监视系统可以被配置为观察场景（如视频帧序列所示），并且随着时间的推移，开发包括对象，动作和行为类别的概念的层级。 也就是说，视频监控系统可以开发逐渐更复杂的抽象级别的模型，用于识别哪些事件和行为是常见的，哪些是不寻常的。 当模型成熟时，视频监控系统会发出异常事件警报。
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10.发明申请公开(公告)号：US20110064268A1
公开(公告)日：2011-03-17
申请号：US12561977
申请日：2009-09-17
CPC分类号： G06K9/6222 , G06K9/00335 , G06K9/00771 , G06K9/6284
摘要： Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.
摘要翻译： 公开了用于视频监控系统的技术，以通过使用交替的聚类和排序层分析像素数据来学习识别复杂行为。 视频监视系统可以被配置为观察场景（如视频帧序列所示），并且随着时间的推移，开发包括对象，动作和行为类别的概念的层级。 也就是说，视频监控系统可以开发逐渐更复杂的抽象级别的模型，用于识别哪些事件和行为是常见的，哪些是不寻常的。 当模型成熟时，视频监控系统会发出异常事件警报。
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