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    • 2. 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。
    • 4. 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。
    • 9. 发明授权
    • Optimal multi-class classifier threshold-offset estimation with particle swarm optimization for visual object recognition
    • 用于视觉对象识别的粒子群优化的最优多类分类器阈值偏移估计
    • US08768868B1
    • 2014-07-01
    • US13440881
    • 2012-04-05
    • Shinko Y. ChengYang ChenDeepak KhoslaKyungnam Kim
    • Shinko Y. ChengYang ChenDeepak KhoslaKyungnam Kim
    • G06N5/00
    • G06N5/00
    • Described is a system for multi-class classifier threshold-offset estimation for visual object recognition. The system receives an input image with input features for classifying. A pair-wise classifier is trained for each pair of a plurality of object classes. A set of classification responses is generated, and a multi-class receiver-operating-characteristics (ROC) curve is computed for a set of threshold-offsets. An objective function of classification performance is computed from the ROC curve and optimized using particle swarm optimization (PSO) to generate a set of optimized threshold-offsets. The optimized threshold-offsets are then applied to the classification responses. The resulting classification responses are compared to a predetermined value to classify each input feature as belonging to one object class or another. The tuning of the threshold-offsets with (PSO) improves classification performance in a visual object recognition system.
    • 描述了用于视觉对象识别的多类分类器阈值偏移估计的系统。 系统接收具有输入特征进行分类的输入图像。 针对多对象类的每一对训练一对成对的分类器。 生成一组分类响应,并计算一组阈值偏移量的多类接收器操作特性(ROC)曲线。 从ROC曲线计算分类性能的目标函数,并使用粒子群优化(PSO)进行优化,以生成一组优化的阈值偏移。 然后将优化的阈值偏移应用于分类响应。 将所得分类响应与预定值进行比较,以将每个输入特征分类为属于一个对象类或另一对象类。 使用(PSO)调整阈值偏移可提高视觉对象识别系统中的分类性能。