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    Blur estimation
    1.
    发明授权
    Blur estimation 有权
    模糊估计

    公开(公告)号：US08494282B2

    公开(公告)日：2013-07-23

    申请号：US12838857

    申请日：2010-07-19

    Applicant: Matthew D. Gaubatz ,  Steven J Simske

    Inventor： Matthew D. Gaubatz ,  Steven J Simske

    IPC: G06K9/40 ,  G02B5/00 ,  G06K19/06

    CPC classification number: G06K7/1469 ,  G06K7/146 ,  G06T5/003 ,  G06T7/136 ,  G06T2207/20201 ,  G06T2207/30176

    Abstract: A method and apparatus for removing blur in an image is disclosed. The blur in the image is caused by relative motion between the imaging device and the object being imaged. A set of differences between the pixel values in the image is calculated. The set of differences in pixel values are divided into two groups, wherein the first group of differences in pixel values corresponds to differences in pixel values due to noise, and the second group of differences in pixel values corresponds to differences in pixel values due to noise and motion. An estimate of the motion blur is determined using the second group of differences in pixel values. The estimate of the blur is then used to remove the blur from an image.

    Abstract translation: 公开了一种用于去除图像中的模糊的方法和装置。 图像中的模糊是由成像装置和成像对象之间的相对运动引起的。 计算图像中像素值之间的一组差异。 像素值的差异集合被分成两组，其中像素值中的第一组差异对应于由于噪声引起的像素值差异，并且第二组像素值对应于由噪声引起的像素值差异 和运动。 使用像素值中的第二组差异来确定运动模糊的估计。 然后使用模糊的估计来从图像中去除模糊。

    Blur Resistant Barcode
    3.
    发明申请
    Blur Resistant Barcode 有权
    抗模糊条形码

    公开(公告)号：US20110073655A1

    公开(公告)日：2011-03-31

    申请号：US12567392

    申请日：2009-09-25

    Applicant: Steven J. Simske ,  Matthew D. Gaubatz ,  Robert Alan Ulichney

    Inventor： Steven J. Simske ,  Matthew D. Gaubatz ,  Robert Alan Ulichney

    IPC: G06K7/10 ,  G06K19/06

    CPC classification number: G06K7/14 ,  G06K19/06028

    Abstract: A blur resistant barcode is disclosed. The blur resistant barcode comprise a plurality of parallel lines and spaces where information is encoded in the barcode by variations in the thicknesses of the plurality of parallel lines and by variations in the spacing between the plurality of parallel lines. The blur resistant barcode has at least one blur resistant feature that has a thickness in an axis of motion greater than a maximum thickness of any one of the plurality of parallel lines. The axis of motion is perpendicular to the plurality of parallel lines.

    Abstract translation: 公开了一种模糊的条形码。 防模糊条形码包括多条平行线和空间，其中信息通过多条平行线的厚度变化以及多条平行线之间的间距的变化而被编码在条形码中。 抗模糊条形码具有至少一个模糊抵抗特征，其具有大于多条平行线中的任何一条的最大厚度的运动轴线的厚度。 运动轴线垂直于多条平行线。

    Resized Image Compression Based on Frequency Content
    7.
    发明申请
    Resized Image Compression Based on Frequency Content 有权
    基于频率内容的图像压缩大小

    公开(公告)号：US20140064631A1

    公开(公告)日：2014-03-06

    申请号：US13600611

    申请日：2012-08-31

    Applicant: Matthew D. Gaubatz ,  Aaron Chan

    Inventor： Matthew D. Gaubatz ,  Aaron Chan

    IPC: G06K9/36

    CPC classification number: H04N19/18 ,  H04N19/124 ,  H04N19/132 ,  H04N19/14 ,  H04N19/176 ,  H04N19/59

    Abstract: Examples disclosed herein relate to compressing an image by resizing an image and compressing the resized image based on frequency content. A processor may resize an image to a target size if the pixel area of the image is greater than the sum of the target pixel area plus a resizing tolerance. The processor may compress the image using a first data removal rule for a portion of the image of a first frequency range and using a second data removal rule for a portion of the image of a second frequency range.

    Abstract translation: 本文公开的示例涉及通过调整图像大小并基于频率内容来压缩调整大小的图像来压缩图像。 如果图像的像素区域大于目标像素区域加上调整大小公差的总和，则处理器可以将图像的大小调整到目标尺寸。 处理器可以使用用于第一频率范围的图像的一部分的第一数据去除规则来压缩图像，并且对于第二频率范围的图像的一部分使用第二数据去除规则。

    System and method for creating a data-bearing image
    8.
    发明授权
    System and method for creating a data-bearing image 有权
    用于创建数据承载图像的系统和方法

    公开(公告)号：US08456699B2

    公开(公告)日：2013-06-04

    申请号：US12563432

    申请日：2009-09-21

    Applicant: Robert Alan Ulichney ,  Steven J. Simske ,  Matthew D. Gaubatz

    Inventor： Robert Alan Ulichney ,  Steven J. Simske ,  Matthew D. Gaubatz

    IPC: G06K15/00 ,  H04N1/40

    CPC classification number: H04N1/4055

    Abstract: A system for creating a data-bearing image can include a reference image generator configured to apply a clustered-dot halftone screen to a continuous-tone image. The resulting reference halftone image includes carrier cells in which a pixel cluster can be shifted to at least two shift positions in the carrier cell. The system also includes a payload encoder configured to segment the data payload and encode data segments into one of the carrier cells by shifting the cluster to one of the shift positions. The system also includes an output device configured to output the resulting data-bearing halftone image.

    Abstract translation: 用于创建数据承载图像的系统可以包括被配置为将聚焦点半色调屏幕应用于连续色调图像的参考图像生成器。 所得到的参考半色调图像包括载波小区，其中像素簇可以移位到载波小区中的至少两个移位位置。 该系统还包括有效载荷编码器，该有效载荷编码器被配置为通过将集群移动到其中一个位移位置来分段数据有效载荷并将数据段编码为载体单元之一。 该系统还包括输出装置，其被配置为输出所得的数据承载半色调图像。

    Automatic redeye detection based on redeye and facial metric values
    9.
    发明授权
    Automatic redeye detection based on redeye and facial metric values 有权
    基于红眼和面部度量值的自动红眼检测

    公开(公告)号：US08446494B2

    公开(公告)日：2013-05-21

    申请号：US12865855

    申请日：2008-02-01

    Applicant: Matthew D. Gaubatz ,  Robert Alan Ulichney

    Inventor： Matthew D. Gaubatz ,  Robert Alan Ulichney

    IPC: H04N5/217

    CPC classification number: H04N1/62 ,  G06K9/00228 ,  G06K9/00604 ,  G06T7/90 ,  G06T2207/30216 ,  H04N1/624

    Abstract: Candidate redeye areas (24) are determined in an input image (20). In this process, a respective set of one or more redeye metric values (28) is associated with each of the candidate redeye areas (24). Candidate face areas (30) are ascertained in the input image (20). In this process, a respective set of one or more face metric values (34) is associated with each of the candidate face areas (30). A respective joint metric vector (78) is assigned to each of the candidate redeye areas (24). The joint metric vector (78) includes metric values that are derived from the respective set of redeye metric values (28) and the set of face metric values (34) associated with a selected one of the candidate face areas (30). Each of one or more of the candidate redeye areas (24) is classified as either a redeye artifact or a non-redeye artifact based on the respective joint metric vector (78) assigned to the candidate redeye area (24).

    Abstract translation: 在输入图像（20）中确定候选红眼区域（24）。 在该过程中，相应的一组一个或多个红眼度量值（28）与候选红眼区域（24）中的每一个相关联。 在输入图像（20）中确定候选面部区域（30）。 在该过程中，一组或多个面部度量值（34）与候选面部区域（30）中的每一个相关联。 各个联合度量矢量（78）被分配给每个候选红眼区域（24）。 联合度量矢量（78）包括从相应的红眼度量值集合（28）和与候选面部区域（30）中选择的一个相关联的面部度量值（34）的集合导出的度量值。 基于分配给候选红眼区域（24）的各个联合度量矢量（78），候选红眼区域（24）中的一个或多个被分类为红眼人造物或非红眼人造物。

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