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    • 1. 发明授权
    • Gesture-controlled interfaces for self-service machines and other applications
    • 用于自助服务机器和其他应用的手势控制接口
    • US07668340B2
    • 2010-02-23
    • US12326540
    • 2008-12-02
    • Charles J. CohenGlenn J. BeachBrook CavellEugene FoulkCharles J. JacobusJay ObermarkGeorge V. Paul
    • Charles J. CohenGlenn J. BeachBrook CavellEugene FoulkCharles J. JacobusJay ObermarkGeorge V. Paul
    • G06K9/00G06F3/033
    • G06K9/00355A63F2300/1093A63F2300/6045G06F3/017G06T7/246
    • A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
    • 公开了一种用于控制自助服务机器和其他设备的手势识别界面。 手势被定义为人类,动物或机器产生的运动和运动姿态。 跟踪特定身体特征,并解释静态和运动手势。 运动手势被定义为参数定界的振荡运动系列,被建模为参数线性参数动态系统,附加的几何约束允许使用少量的存储器和处理时间进行实时识别。 优选使用线性最小二乘法来确定表示每个姿态的参数。 特征位置测量结合使用手势参数种子的预测器仓组,并且系统确定哪个箱最适合观察到的运动。 识别静态姿势手势优选地通过将来自图像的其余部分的身体/物体定位,描述该对象并识别该描述来执行。 本公开详细描述了用于手势识别的方法,以及用于使用手势识别来控制设备(包括自助服务机器)的整体架构。
    • 2. 发明授权
    • Gesture-controlled interfaces for self-service machines and other applications
    • 用于自助服务机器和其他应用的手势控制接口
    • US06950534B2
    • 2005-09-27
    • US10759459
    • 2004-01-16
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • G06F3/00G06F3/01G06K9/00G06T7/20
    • G06K9/00355A63F2300/1093A63F2300/6045G06F3/017G06T7/246
    • A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
    • 公开了一种用于控制自助服务机器和其他设备的手势识别界面。 手势被定义为人类,动物或机器产生的运动和运动姿态。 跟踪特定身体特征,并解释静态和运动手势。 运动手势被定义为参数定界的振荡运动系列,被建模为参数线性参数动态系统,附加的几何约束允许使用少量的存储器和处理时间进行实时识别。 优选使用线性最小二乘法来确定表示每个姿态的参数。 特征位置测量结合使用手势参数种子的预测器仓组,并且系统确定哪个箱最适合观察到的运动。 识别静态姿势手势优选地通过将来自图像的其余部分的身体/物体定位,描述该对象并识别该描述来执行。 本公开详细描述了用于手势识别的方法,以及用于使用手势识别来控制设备(包括自助服务机器)的整体架构。
    • 5. 发明申请
    • GESTURE-CONTROLLED INTERFACES FOR SELF-SERVICE MACHINES AND OTHER APPLICATIONS
    • 用于自助服务机器和其他应用程序的控制接口
    • US20090074248A1
    • 2009-03-19
    • US12326540
    • 2008-12-02
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • G06K9/00
    • G06K9/00355A63F2300/1093A63F2300/6045G06F3/017G06T7/246
    • A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
    • 公开了一种用于控制自助服务机器和其他设备的手势识别界面。 手势被定义为人类,动物或机器产生的运动和运动姿态。 跟踪特定身体特征,并解释静态和运动手势。 运动手势被定义为参数定界的振荡运动系列,被建模为参数线性参数动态系统,附加的几何约束允许使用少量的存储器和处理时间进行实时识别。 优选使用线性最小二乘法来确定表示每个姿态的参数。 特征位置测量结合使用手势参数种子的预测器仓组,并且系统确定哪个箱最适合观察到的运动。 识别静态姿势手势优选地通过将来自图像的其余部分的身体/物体定位,描述该对象并识别该描述来执行。 本公开详细描述了用于手势识别的方法,以及用于使用手势识别来控制设备(包括自助服务机器)的整体架构。
    • 6. 发明申请
    • Gesture-controlled interfaces for self-service machines and other applications
    • 用于自助服务机器和其他应用的手势控制接口
    • US20060013440A1
    • 2006-01-19
    • US11226831
    • 2005-09-14
    • Charles CohenGlenn BeachBrook CavellGene FoulkCharles JacobusJay ObermarkGeorge Paul
    • Charles CohenGlenn BeachBrook CavellGene FoulkCharles JacobusJay ObermarkGeorge Paul
    • G06K9/00
    • G06K9/00355A63F2300/1093A63F2300/6045G06F3/017G06T7/246
    • A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
    • 公开了一种用于控制自助服务机器和其他设备的手势识别界面。 手势被定义为人类,动物或机器产生的运动和运动姿态。 跟踪特定身体特征,并解释静态和运动手势。 运动手势被定义为参数定界的振荡运动系列,被建模为参数线性参数动态系统,附加的几何约束允许使用少量的存储器和处理时间进行实时识别。 优选使用线性最小二乘法来确定表示每个姿态的参数。 特征位置测量结合使用手势参数种子的预测器仓组,并且系统确定哪个箱最适合观察到的运动。 识别静态姿势手势优选地通过将来自图像的其余部分的身体/物体定位,描述该对象并识别该描述来执行。 本公开详细描述了用于手势识别的方法,以及用于使用手势识别来控制设备(包括自助服务机器)的整体架构。
    • 7. 发明授权
    • Gesture-controlled interfaces for self-service machines and other applications
    • 用于自助服务机器和其他应用的手势控制接口
    • US06681031B2
    • 2004-01-20
    • US09371460
    • 1999-08-10
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • G06K900
    • G06F3/017A63F2300/1093A63F2300/6045G06K9/00355G06T7/246G07F9/023
    • A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
    • 公开了一种用于控制自助服务机器和其他设备的手势识别界面。 手势被定义为人类,动物或机器产生的运动和运动姿态。 跟踪特定身体特征,并解释静态和运动手势。 运动手势被定义为参数定界的振荡运动系列,被建模为参数线性参数动态系统,附加的几何约束允许使用少量的存储器和处理时间进行实时识别。 优选使用线性最小二乘法来确定表示每个姿态的参数。 特征位置测量结合使用手势参数种子的预测器仓组,并且系统确定哪个箱最适合观察到的运动。 识别静态姿势手势优选地通过将来自图像的其余部分的身体/物体定位,描述该对象并识别该描述来执行。 本公开详细描述了用于手势识别的方法,以及用于使用手势识别来控制设备(包括自助服务机器)的整体架构。
    • 9. 发明授权
    • Gesture-controlled interfaces for self-service machines and other applications
    • 用于自助服务机器和其他应用的手势控制接口
    • US07460690B2
    • 2008-12-02
    • US11226831
    • 2005-09-14
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • Charles J. CohenGlenn BeachBrook CavellGene FoulkCharles J. JacobusJay ObermarkGeorge Paul
    • G06K9/00G06K9/36G06F3/033
    • G06K9/00355A63F2300/1093A63F2300/6045G06F3/017G06T7/246
    • A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
    • 公开了一种用于控制自助服务机器和其他设备的手势识别界面。 手势被定义为人类,动物或机器产生的运动和运动姿态。 跟踪特定身体特征,并解释静态和运动手势。 运动手势被定义为参数定界的振荡运动系列,被建模为参数线性参数动态系统,附加的几何约束允许使用少量的存储器和处理时间进行实时识别。 优选使用线性最小二乘法来确定表示每个姿态的参数。 特征位置测量结合使用手势参数种子的预测器仓组,并且系统确定哪个箱最适合观察到的运动。 识别静态姿势手势优选地通过将来自图像的其余部分的身体/物体定位,描述该对象并识别该描述来执行。 本公开详细描述了用于手势识别的方法,以及用于使用手势识别来控制设备(包括自助服务机器)的整体架构。