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    • 3. 发明授权
    • Weighing platform with computer-vision tracking
    • 称重平台与计算机视觉跟踪
    • US09121751B2
    • 2015-09-01
    • US13296950
    • 2011-11-15
    • David J. Michael
    • David J. Michael
    • G01G23/38G01G19/03G01G19/393G01G19/387
    • G01G23/38G01G19/035G01G19/387G01G19/393
    • The disclosure relates to weighing moving objects in a weighing platform functionally coupled to a computer-vision tracking platform. The objects can translate, rotate, and translate and rotate. Weighing of the objects can be accomplished through combination of object imaging and upstream weighing. Object imaging can permit tracking, through computer vision, a logical object moving in a trajectory from the first location to the second location, wherein a logical object is a formal representation of one or more physical objects. Upstream weighing can permit updating a record indicative of weight of the one or more physical objects associated with the tracked logical object. As a part of weighing termination, data integrity check(s) can be performed on a plurality of records indicative of a weight of a single physical object. Based on outcome of the data integrity check(s), a record indicative of the weight of the single physical object can be supplied.
    • 本公开涉及在功能上耦合到计算机视觉跟踪平台的称重平台中称量移动物体。 对象可以进行平移,旋转,平移和旋转。 物体的称重可以通过对象成像和上游称重的组合来实现。 对象成像可以允许通过计算机视觉跟踪从第一位置到第二位置的轨迹移动的逻辑对象,其中逻辑对象是一个或多个物理对象的正式表示。 上游称重可以允许更新指示与跟踪的逻辑对象相关联的一个或多个物理对象的权重的记录。 作为称重终端的一部分,可以在指示单个物理对象的权重的多个记录上执行数据完整性检查。 基于数据完整性检查的结果,可以提供表示单个物理对象的权重的记录。
    • 4. 发明申请
    • SYSTEM AND METHOD FOR LOCATING A THREE-DIMENSIONAL OBJECT USING MACHINE VISION
    • 使用机器视觉定位三维物体的系统和方法
    • US20080298672A1
    • 2008-12-04
    • US11754948
    • 2007-05-29
    • Aaron S. WallackDavid J. Michael
    • Aaron S. WallackDavid J. Michael
    • G06K9/00G06K9/62
    • G06K9/32G06T7/73G06T2207/30164
    • This invention provides a system and method for determining position of a viewed object in three dimensions by employing 2D machine vision processes on each of a plurality of planar faces of the object, and thereby refining the location of the object. First a rough pose estimate of the object is derived. This rough pose estimate can be based upon predetermined pose data, or can be derived by acquiring a plurality of planar face poses of the object (using, for example multiple cameras) and correlating the corners of the trained image pattern, which have known coordinates relative to the origin, to the acquired patterns. Once the rough pose is achieved, this is refined by defining the pose as a quaternion (a, b, c and d) for rotation and a three variables (x, y, z) for translation and employing an iterative weighted, least squares error calculation to minimize the error between the edgelets of trained model image and the acquired runtime edgelets. The overall, refined/optimized pose estimate incorporates data from each of the cameras' acquired images. Thereby, the estimate minimizes the total error between the edgelets of each camera's/view's trained model image and the associated camera's/view's acquired runtime edgelets. A final transformation of trained features relative to the runtime features is derived from the iterative error computation.
    • 本发明提供了一种用于通过在对象的多个平面中的每一个上采用2D机器视觉过程来确定三维视图对象的位置的系统和方法,从而改善对象的位置。 首先推导出对象的粗略姿态估计。 这种粗略姿态估计可以基于预定的姿态数据,或者可以通过获取对象的多个平面脸部姿势(使用例如多个摄像机)并且将已训练图像图案的角部相关联来获得,其具有已知的坐标相对 到原产地,获得的模式。 一旦实现了粗糙的姿态,就通过将姿态定义为旋转的四元数(a,b,c和d)和用于平移的三个变量(x,y,z)并采用迭代加权最小二乘误差 计算以最小化训练模型图像的边缘与所获取的运行时间边缘之间的误差。 整体,精细/优化的姿态估计包含来自每个摄像机所获取图像的数据。 因此,估计使每个相机/视图的训练模型图像的边缘与相关联的相机/视图的获取的运行时间边缘之间的总误差最小化。 相对于运行时特征的训练特征的最终变换是从迭代误差计算得出的。
    • 8. 发明授权
    • System and method for locating a three-dimensional object using machine vision
    • 使用机器视觉定位三维物体的系统和方法
    • US08126260B2
    • 2012-02-28
    • US11754948
    • 2007-05-29
    • Aaron S. WallackDavid J. Michael
    • Aaron S. WallackDavid J. Michael
    • G06K9/00
    • G06K9/32G06T7/73G06T2207/30164
    • This invention provides a system and method for determining position of a viewed object in three dimensions by employing 2D machine vision processes on each of a plurality of planar faces of the object, and thereby refining the location of the object. First a rough pose estimate of the object is derived. This rough pose estimate can be based upon predetermined pose data, or can be derived by acquiring a plurality of planar face poses of the object (using, for example multiple cameras) and correlating the corners of the trained image pattern, which have known coordinates relative to the origin, to the acquired patterns. Once the rough pose is achieved, this is refined by defining the pose as a quaternion (a, b, c and d) for rotation and a three variables (x, y, z) for translation and employing an iterative weighted, least squares error calculation to minimize the error between the edgelets of trained model image and the acquired runtime edgelets. The overall, refined/optimized pose estimate incorporates data from each of the cameras' acquired images. Thereby, the estimate minimizes the total error between the edgelets of each camera's/view's trained model image and the associated camera's/view's acquired runtime edgelets. A final transformation of trained features relative to the runtime features is derived from the iterative error computation.
    • 本发明提供了一种用于通过在对象的多个平面中的每一个上采用2D机器视觉过程来确定三维视图对象的位置的系统和方法,从而改善对象的位置。 首先推导出对象的粗略姿态估计。 这种粗略姿态估计可以基于预定的姿态数据,或者可以通过获取对象的多个平面脸部姿势(使用例如多个摄像机)并且将已训练图像图案的角部相关联来获得,其具有已知的坐标相对 到原产地,获得的模式。 一旦实现了粗糙的姿态,就通过将姿态定义为旋转的四元数(a,b,c和d)和用于平移的三个变量(x,y,z)并采用迭代加权最小二乘误差 计算以最小化训练模型图像的边缘与所获取的运行时间边缘之间的误差。 整体,精细/优化的姿态估计包含来自每个摄像机所获取图像的数据。 因此,估计使每个相机/视图的训练模型图像的边缘与相关联的相机/视图的获取的运行时间边缘之间的总误差最小化。 相对于运行时特征的训练特征的最终变换是从迭代误差计算得出的。
    • 9. 发明申请
    • MACHINE VISION TECHNIQUE FOR MANUFACTURING SEMICONDUCTOR WAFERS
    • 制造半导体波长的机器视觉技术
    • US20090274361A1
    • 2009-11-05
    • US12113492
    • 2008-05-01
    • John W. SchwabGang LiuDavid J. Michael
    • John W. SchwabGang LiuDavid J. Michael
    • G06K9/00
    • H01L31/022425H01L21/681H01L21/682H01L31/18Y02E10/50
    • A vision system is provided to determine a positional relationship between a photovoltaic device wafer on a platen and a printing element, such as a printing screen, on a remote side of the photovoltaic device wafer from the platen. A source emits ultraviolet light along a path that is transverse to a longitudinal axis of an aperture through the platen, and a diffuser panel is located along that path. A reflector directs the light from the diffuser panel toward the aperture. A video camera is located along the longitudinal axis of the aperture and produces an image using light received from the platen aperture, wherein some of that received light was reflected by the wafer. A band-pass filter is placed in front of the camera to block ambient light. The use of diffused ultraviolet light enhances contrast in the image between the wafer and the printing element.
    • 提供了一种视觉系统,用于确定平台上的光伏器件晶片与光伏器件晶片远离印版的远程侧的印刷元件(例如印刷丝网)之间的位置关系。 源沿着与穿过压板的孔的纵向轴线横向的路径发射紫外光,并且漫射板位于沿着该路径的位置。 反射器将来自扩散板的光引向光圈。 摄像机沿着孔的纵向轴线定位,并使用从压板孔接收的光产生图像,其中一些接收的光被晶片反射。 带通滤波器放置在相机的前面以阻挡环境光。 使用扩散的紫外光增强了晶片和印刷元件之间的图像的对比度。
    • 10. 发明授权
    • Automated inspection of objects undergoing general affine transformation
    • 对进行一般仿射变换的物体进行自动检查
    • US06421458B2
    • 2002-07-16
    • US09141932
    • 1998-08-28
    • David J. MichaelIgor Reyzin
    • David J. MichaelIgor Reyzin
    • G06K932
    • G06K9/00G06T7/001G06T7/33G06T2207/30108
    • During statistical training and automated inspection of objects by a machine vision system, a General Affine Transform is advantageously employed to improve system performance. During statistical training, the affine poses of a plurality of training images are determined with respect to an alignment model image. Following filtering to remove high frequency content, the training images and their corresponding affine poses are applied to an affine transformation. The resulting transformed images are accumulated to compute template and threshold images to be used for run-time inspection. During run-time inspection, the affine pose of the run-time image relative to the alignment model image is determined. Following filtering of the run-time image, the run-time image is affine transformed by its affine pose. The resulting transform image is compared with the template and threshold images computed during statistical training to determine object status. In this manner, automated training and inspection is relatively less demanding on system storage, and results in an improvement in system speed and accuracy.
    • 在通过机器视觉系统的统计训练和物体的自动检查期间,有利地采用通用仿射变换来提高系统性能。 在统计训练期间,相对于对准模型图像确定多个训练图像的仿射姿态。 进行滤波以去除高频内容后,将训练图像及其对应的仿射姿态应用于仿射变换。 累积所得到的变换图像以计算用于运行时检查的模板和阈值图像。 在运行时检查期间,确定运行时图像相对于对准模型图像的仿射姿态。 在运行时图像过滤之后,运行时图像通过其仿射姿态进行仿射变换。 将所得到的变换图像与在统计训练期间计算的模板和阈值图像进行比较,以确定对象状态。 以这种方式,自动化培训和检查对系统存储的要求相对较低,并且导致系统速度和精度的提高。