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    • 1. 发明授权
    • Hierarchical video search and recognition system
    • 分层视频搜索和识别系统
    • US08874584B1
    • 2014-10-28
    • US12660320
    • 2010-02-24
    • Yang ChenSwarup MedasaniDavid L. AllenQin JiangYuri OwechkoTsai-Ching Lu
    • Yang ChenSwarup MedasaniDavid L. AllenQin JiangYuri OwechkoTsai-Ching Lu
    • G06F17/30
    • G06F17/30805G06F17/30811
    • 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.
    • 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。
    • 3. 发明申请
    • Vision System for Monitoring Humans in Dynamic Environments
    • 动态环境监测人的视觉系统
    • US20110050878A1
    • 2011-03-03
    • US12549425
    • 2009-08-28
    • James W. WellsRoland J. MenassaCharles W. Wampler, IISwarup MedasaniYuri OwechkoKyungnam KimYang Chen
    • James W. WellsRoland J. MenassaCharles W. Wampler, IISwarup MedasaniYuri OwechkoKyungnam KimYang Chen
    • H04N7/18
    • H04N7/181
    • A safety monitoring system for a workspace area. The workspace area related to a region having automated moveable equipment. A plurality of vision-based imaging devices capturing time-synchronized image data of the workspace area. Each vision-based imaging device repeatedly capturing a time synchronized image of the workspace area from a respective viewpoint that is substantially different from the other respective vision-based imaging devices. A visual processing unit for analyzing the time-synchronized image data. The visual processing unit processes the captured image data for identifying a human from a non-human object within the workspace area. The visual processing unit further determining potential interactions between a human and the automated moveable equipment. The visual processing unit further generating control signals for enabling dynamic reconfiguration of the automated moveable equipment based on the potential interactions between the human and the automated moveable equipment in the workspace area.
    • 用于工作区的安全监控系统。 与具有自动移动设备的区域相关的工作空间区域。 多个基于视觉的成像设备捕获工作区域的时间同步图像数据。 每个基于视觉的成像设备从与其他各自的基于视觉的成像设备基本上不同的相应视点重复地捕获工作区域的时间同步图像。 一种用于分析时间同步图像数据的可视处理单元。 视觉处理单元从工作区域内的非人物对象处理用于识别人的拍摄图像数据。 视觉处理单元进一步确定人与自动移动设备之间的潜在交互作用。 视觉处理单元还基于人与工作空间区域中的自动移动设备之间的潜在交互,进一步产生用于实现自动移动设备的动态重新配置的控制信号。
    • 4. 发明授权
    • Method for particle swarm optimization with random walk
    • 随机散乱的粒子群优化方法
    • US08793200B1
    • 2014-07-29
    • US12586505
    • 2009-09-22
    • Yang ChenYuri OwechkoSwarup Medasani
    • Yang ChenYuri OwechkoSwarup Medasani
    • G06N5/00
    • G06N5/003G06N3/006
    • Described is a method for particle swarm optimization (PSO) utilizing a random walk process. A plurality of software agents is configured to operate as a cooperative swarm to locate an optimum of an objective function. The method described herein comprises two phases. In a first phase, the plurality of software agents randomly explores the multi-dimensional solution space by undergoing a Brownian motion style random walk process. In a second phase, the velocity and position vectors for each particle are updated probabilistically according to a PSO algorithm. By allowing the particles to undergo a random walk phase, the particles have an increased opportunity to explore their neighborhood, land in the neighborhood of a true optimum, and avoid prematurely converging on a sub-optimum. The present invention improves on what is currently known by increasing the success rate of the PSO algorithm in addition to reducing the required computation.
    • 描述了利用随机游走过程的粒子群优化(PSO)的方法。 多个软件代理被配置为作为协作群操作以定位目标函数的最优。 本文描述的方法包括两个阶段。 在第一阶段,多个软件代理人通过经历布朗运动风格随机游走过程随机探索多维解决方案空间。 在第二阶段,根据PSO算法概率地更新每个粒子的速度和位置向量。 通过允许颗粒经历随机游走阶段,颗粒具有增加的机会来探索它们的邻域,附近的真实最优值,并避免过早地收敛于次优。 除了减少所需的计算之外,本发明通过增加PSO算法的成功率来改进当前所知道的内容。
    • 5. 发明授权
    • Method for image registration utilizing particle swarm optimization
    • 使用粒子群优化的图像配准方法
    • US08645294B1
    • 2014-02-04
    • US12583238
    • 2009-08-17
    • Yuri OwechkoYang ChenSwarup Medasani
    • Yuri OwechkoYang ChenSwarup Medasani
    • G06F15/18
    • G06N5/043G06K9/6229G06N3/006G06T7/337G06T7/35
    • Described is a method for image registration utilizing particle swarm optimization (PSO). In order to register two images, a set of image windows is first selected from a test image and transformed. A plurality of software agents is configured to operate as a cooperative swarm to optimize an objective function, and an objective function is then evaluated at the location of each agent. The objective function represents a measure of the difference or registration quality between at least one transformed image window and a reference image. The position vectors representing the current individual best solution found and the current global best solution found by all agents are then updated according to PSO dynamics. Finally, the current global best solution is compared with a maximum pixel value which signifies a match between an image window and the reference image. A system and a computer program product are also described.
    • 描述了使用粒子群优化(PSO)的图像配准的方法。 为了注册两个图像,首先从测试图像中选择一组图像窗口并进行变换。 多个软件代理被配置为作为协作群来操作以优化目标函数,然后在每个代理的位置处评估目标函数。 目标函数表示至少一个变换的图像窗口和参考图像之间的差异或注册质量的度量。 然后根据PSO动态更新表示当前找到的最佳解决方案的位置向量和所有代理发现的当前全局最佳解。 最后,将当前全局最佳解决方案与表示图像窗口和参考图像之间的匹配的最大像素值进行比较。 还描述了系统和计算机程序产品。
    • 6. 发明授权
    • Vision system for monitoring humans in dynamic environments
    • 用于在动态环境中监测人的视觉系统
    • US08253792B2
    • 2012-08-28
    • US12549425
    • 2009-08-28
    • James W. WellsRoland J. MenassaCharles W. Wampler, IISwarup MedasaniYuri OwechkoKyungnam KimYang Chen
    • James W. WellsRoland J. MenassaCharles W. Wampler, IISwarup MedasaniYuri OwechkoKyungnam KimYang Chen
    • H04N9/47
    • H04N7/181
    • A safety monitoring system for a workspace area. The workspace area related to a region having automated moveable equipment. A plurality of vision-based imaging devices capturing time-synchronized image data of the workspace area. Each vision-based imaging device repeatedly capturing a time synchronized image of the workspace area from a respective viewpoint that is substantially different from the other respective vision-based imaging devices. A visual processing unit for analyzing the time-synchronized image data. The visual processing unit processes the captured image data for identifying a human from a non-human object within the workspace area. The visual processing unit further determining potential interactions between a human and the automated moveable equipment. The visual processing unit further generating control signals for enabling dynamic reconfiguration of the automated moveable equipment based on the potential interactions between the human and the automated moveable equipment in the workspace area.
    • 用于工作区的安全监控系统。 与具有自动移动设备的区域相关的工作空间区域。 多个基于视觉的成像设备捕获工作区域的时间同步图像数据。 每个基于视觉的成像设备从与其他各自的基于视觉的成像设备基本上不同的相应视点重复地捕获工作区域的时间同步图像。 一种用于分析时间同步图像数据的可视处理单元。 视觉处理单元从工作区域内的非人物对象处理用于识别人的拍摄图像数据。 视觉处理单元进一步确定人与自动移动设备之间的潜在交互作用。 视觉处理单元还基于人与工作空间区域中的自动移动设备之间的潜在交互,进一步产生用于实现自动移动设备的动态重新配置的控制信号。
    • 7. 发明授权
    • System for automatic data clustering utilizing bio-inspired computing models
    • 使用生物启发计算模型的自动数据聚类系统
    • US09009156B1
    • 2015-04-14
    • US12590574
    • 2009-11-10
    • Qin JiangYang Chen
    • Qin JiangYang Chen
    • G06F17/30
    • G06F15/1735G06N3/0409G06N3/0418G06N3/0436
    • Described is a system for automatic data clustering which utilizes bio-inspired computing models. The system performs operations of mapping a set of input data into a feature space using a bio-inspired computing model. A number of clusters inside the set of input data is then determined by finding an optimal vigilance parameter using a bio-inspired computing model. Finally, the set of input data is clustered based on the determined number of clusters. The input data is mapped with a Freeman's KIII network, such that each data point is mapped into a KIII network response. Furthermore, the number of clusters is determined using the fuzzy adaptive resonance theory (ART), and the data is clustered using the fuzzy c-means method. Clustering quality measures are used to compute an objective function to evaluate the quality of clustering.
    • 描述了一种利用生物启发计算模型的自动数据聚类系统。 系统执行使用生物启发的计算模型将一组输入数据映射到特征空间的操作。 然后通过使用生物启发的计算模型找到最佳警戒参数来确定输入数据集合内的多个群集。 最后,基于所确定的群集数量对该组输入数据进行聚类。 输入数据用Freeman的KIII网络映射,使得每个数据点被映射到KIII网络响应。 此外,使用模糊自适应共振理论(ART)确定簇的数量,并且使用模糊c-means方法对数据进行聚类。 聚类质量测度用于计算目标函数,以评估聚类质量。
    • 8. 发明授权
    • Valuation-based learning system
    • 基于估值的学习系统
    • US08032467B1
    • 2011-10-04
    • US12156447
    • 2008-05-31
    • Yang ChenQin JiangDavid Shu
    • Yang ChenQin JiangDavid Shu
    • G06F15/18
    • G06N99/005
    • The present invention relates to a valuation-based learning system. The system is configured to receive a plurality of inputs, each input being input evidence corresponding to a variable in a Dempster-Shafer Reasoning System. The Dempster-Shafer Reasoning System is a network of interconnected nodes, with each node representing a variable that is representative of a characteristic of a problem domain. A discount weight is then optimized for assigning to each of the inputs. A basic probability assignment (bpa) is generated using the Dempster-Shafer Reasoning System, and where the bpa is an output for use in determining a solution of the problem domain. Finally, a solution to the problem domain is determined using the bpa.
    • 本发明涉及基于估值的学习系统。 该系统被配置为接收多个输入,每个输入是对应于Dempster-Shafer推理系统中的变量的输入证据。 Dempster-Shafer推理系统是互连节点网络,每个节点代表一个代表问题域特征的变量。 然后优化折扣权重以分配给每个输入。 使用Dempster-Shafer推理系统生成基本概率分配(bpa),其中bpa是用于确定问题域的解决方案的输出。 最后,使用bpa确定问题域的解决方案。