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    Hierarchical watermark detector
    22.
    发明授权
    Hierarchical watermark detector 有权

    公开(公告)号：US09898792B2

    公开(公告)日：2018-02-20

    申请号：US15232388

    申请日：2016-08-09

    Applicant: Digimarc Corporation

    Inventor： Osama M. Alattar ,  Adnan M. Alattar

    IPC: G06K9/00 ,  G06T1/00 ,  H04N1/32

    CPC classification number: G06T1/005 ,  G06K9/00744 ,  G06T2201/0051 ,  G06T2201/0061 ,  G06T2201/0065 ,  H04N1/32203 ,  H04N1/32267 ,  H04N1/32288 ,  H04N1/32352

    Abstract: The present invention relates generally to digital watermarking. One aspect of the disclosure includes a method comprising: obtaining data representing imagery; using one or more configured processors, analyzing a plurality of portions of the data to detect a watermark orientation component, said analyzing employing a match filter, in which the match filter yields a correlation value for each of the plurality of portions; determining a first portion from the plurality of portions that comprises a correlation value meeting a predetermined value; and directing a watermark decoder at the first portion to decode a plural-bit watermark payload, in which the watermark decoder produces a watermark signature for the first portion, and in which the watermark decoder searches a plurality of areas at or around the first portion to decode the plural-bit watermark payload. Of course, many other aspects and disclosure are provided in this patent document.

    LEARNING SYSTEMS AND METHODS
    23.
    发明申请
    LEARNING SYSTEMS AND METHODS 审中-公开

    公开(公告)号：US20170243317A1

    公开(公告)日：2017-08-24

    申请号：US15446811

    申请日：2017-03-01

    Applicant: Digimarc Corporation

    Inventor： Tony F. Rodriguez ,  Osama M. Alattar ,  Hugh L. Brunk ,  Joel R. Meyer ,  William Y. Conwell ,  Ajith Mulki Kamath

    IPC: G06T1/00 ,  G06K9/62

    CPC classification number: G06T1/0021 ,  G06F21/16 ,  G06K9/00671 ,  G06K9/3216 ,  G06K9/6256 ,  G06K9/6259 ,  G06K9/627

    Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

    Signal Processors and Methods for Estimating Geometric Transformations of Images for Digital Data Extraction
    24.
    发明申请
    Signal Processors and Methods for Estimating Geometric Transformations of Images for Digital Data Extraction 有权

    公开(公告)号：US20170193628A1

    公开(公告)日：2017-07-06

    申请号：US15211944

    申请日：2016-07-15

    Applicant: Digimarc Corporation

    Inventor： Ravi K. Sharma ,  John D. Lord ,  Robert G. Lyons ,  Osama M. Alattar ,  Jacob L. Boles

    IPC: G06T1/00 ,  G06K9/62 ,  G06T3/00

    CPC classification number: G06T1/0064 ,  G06K9/6206 ,  G06T3/0006 ,  G06T3/0056 ,  G06T2201/0052 ,  G06T2201/0065 ,  G06T2201/0081

    Abstract: Signal processing devices and methods estimate a geometric transform of an image signal. From a seed set of transform candidates, a direct least squares method applies a seed transform candidate to a reference signal and then measures correlation between the transformed reference signal and an image signal in which the reference signal is encoded. Geometric transform candidates encompass differential scale and shear, which are useful in approximating a perspective transform. For each candidate, update coordinates of reference signal features are identified in the image signal and provided as input to a least squares method to compute an update to the transform candidate. The method iterates so long as the update of the transform provides a better correlation. At the end of the process, the method identifies a geometric transform or set of top transforms based on a further analysis of correlation, as well as other results. Phase characteristics are exploited in the process of updating coordinates and measuring correlation. The geometric transform is used as an approximation of the geometric distortion of an image after digital data is encoded in it, and is used to compensate for this distortion to facilitate extracting embedded digital messages from the image. Due to the errors in the approximation, a signal confidence metric is determined and used to weight message symbol estimates extracted from the image.

    LEARNING SYSTEMS AND METHODS
    25.
    发明申请
    LEARNING SYSTEMS AND METHODS 有权
    学习系统与方法

    公开(公告)号：US20150055855A1

    公开(公告)日：2015-02-26

    申请号：US14449821

    申请日：2014-08-01

    Applicant: Digimarc Corporation

    Inventor： Tony F. Rodriguez ,  Osama M. Alattar ,  Hugh L. Brunk ,  Joel R. Meyer ,  William Y. Conwell ,  Ajith Mulki Kamath

    IPC: G06K9/62

    CPC classification number: G06T1/0021 ,  G06F21/16 ,  G06K9/00671 ,  G06K9/3216 ,  G06K9/6256 ,  G06K9/6259 ,  G06K9/627

    Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

    Abstract translation: 描绘对象的图像序列例如通过零售商店中的销售点终端处的相机被捕获。 识别对象，例如通过从一个或多个图像检测到的条形码或水印。 一旦对象的身份被知道，这样的信息被用于训练分类器（例如，机器学习系统）以从其他捕获的图像识别对象，包括可能由于模糊，劣质照明等而降级的图像。在另一个 处理这种退化的图像以识别在对象的基于指纹的识别中有用的特征点。 从这种退化的图像提取的特征点有助于在现实生活环境下的对象的基于指纹的识别，与从原始图像提取的特征点（例如，包含用于这些对象的标签图案的数字文件）相反。 其他各种功能和布置也有所不同，其中一些涉及设计分类器，以防止分类器复制。

    Methods and arrangements for sorting items, useful in recycling
    29.
    发明授权
    Methods and arrangements for sorting items, useful in recycling 有权

    公开(公告)号：US11878327B2

    公开(公告)日：2024-01-23

    申请号：US17470674

    申请日：2021-09-09

    Applicant: Digimarc Corporation

    Inventor： Ravi K Sharma ,  Tomas Filler ,  Vojtech Holub ,  Osama M. Alattar ,  Hugh L. Brunk ,  John D. Lord ,  Geoffrey B. Rhoads ,  William Y. Conwell ,  Ajith M. Kamath

    IPC: B07C5/34 ,  B07C5/342

    CPC classification number: B07C5/3412 ,  B07C5/3422 ,  B07C2501/0045

    Abstract: A plastic item, such as a beverage bottle, can convey two distinct digital watermarks, encoded using two distinct signaling protocols. A first, printed label watermark conveys a retailing payload, including a Global Trade Item Number (GTIN) used by a point-of-sale scanner in a retail store to identify and price the item when presented for checkout. A second, plastic texture watermark may convey a recycling payload, including data identifying the composition of the plastic. The use of two different signaling protocols assures that a point-of-sale scanner will not spend its limited time and computational resources working to decode the recycling watermark, which may lack data needed for retail checkout. In some embodiments, a recycling apparatus makes advantageous use of both types of watermarks to identify the plastic composition of the item (e.g., relating GTIN to plastic type using an associated database), thereby increasing the fraction of items that are correctly identified for sorting and recycling. In other embodiments the plastic item (or a label thereon) bears only a single watermark. A great number of other features and arrangements are also detailed.

    LEARNING SYSTEMS AND METHODS
    30.
    发明申请
    LEARNING SYSTEMS AND METHODS 有权

    公开(公告)号：US20220270199A1

    公开(公告)日：2022-08-25

    申请号：US17694396

    申请日：2022-03-14

    Applicant: Digimarc Corporation

    Inventor： Tony F. Rodriguez ,  Osama M. Alattar ,  Hugh L. Brunk ,  Joel R. Meyer ,  William Y. Conwell ,  Ajith M. Kamath

    IPC: G06T1/00 ,  G06F21/16 ,  G06K9/62 ,  G06V10/24 ,  G06V20/20

    Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects). A great variety of other features and arrangements—some involving designing classifiers so as to combat classifier copying—are also detailed.

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