Digimarc Corporation

Digimarc Coporation

Osama M Alattar

Ravi K Sharma

Hugh L Brunk

Vojtech Holub

John Douglas Lord

William Y Conwell

Adnan M Alattar

Ajith Mulki Kamath

Robert G Lyons

Tomas Filler

Tony F Rodriguez

Brett Alan Bradley

Shankar Thagadur Shivappa

Scott M Long

Aparna R Gurijala

Jacob L Boles

Geoffrey B Rhoads

Alastair M Reed

Joel R Meyer

Kenneth Lee Levy

Trent J Brundage

Brett T Hannigan

Ammon E Gustafson

Matthew M Weaver

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.

Learning systems and methods

A plastic item, such as a beverage bottle, conveys 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 conveys 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 lacks the 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. A great number of other features and arrangements are also detailed.

Recycling methods and systems, and related plastic containers

Signal processing devices and methods estimate a geometric transform of an image. One method measures correlation between a transformed reference signal component and an image, updates, based on the measured correlation that is below a threshold, coordinates of the reference signal component to a location within a neighborhood around the coordinates of the transformed reference signal component; determines a new geometric transform that transforms the reference signal components to updated coordinates; and applies the new geometric transform to extract encoded digital data from the image.

Signal processors and methods for estimating geometric transformations of images for digital data extraction

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.

Methods and arrangements for sorting items, useful in recycling

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&#x2014;some involving designing classifiers so as to combat classifier copying&#x2014;are also detailed.

Signal processing devices and methods obtain an estimate of a geometric transform of an image signal, use it to obtain a measure of signal confidence of a reference signal in a sub-block of the image signal, and weight message estimates based on the signal confidence. The geometric transform is used as an approximation of the geometric distortion of an image after digital data is encoded in it, and it is used to compensate for this distortion to facilitate extracting embedded digital messages from the image. Due to the errors in the approximation, the signal confidence metric is determined and used to weight message symbol estimates extracted from the image.

Methods and arrangements involving electronic devices, such as smartphones, tablet computers, wearable devices, etc., are disclosed. One arrangement involves a low-power processing technique for discerning cues from audio input. Another involves a technique for detecting audio activity based on the Kullback-Liebler divergence (KLD) (or a modified version thereof) of the audio input. Still other arrangements concern techniques for managing the manner in which policies are embodied on an electronic device. Others relate to distributed computing techniques. A great variety of other features are also detailed.

Methods and system for cue detection from audio input, low-power data processing and related arrangements

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 redundantly embedded watermark signal, the analyzing producing detection statistics for each of the plurality of portions, the detection statistics comprising a payload signature, a rotation angle and a scale factor for each portion of the plurality of portions; accumulating payload signatures based on compatible rotation angles and scale factors, said accumulating yielding an accumulated payload signature; and decoding a plural-bit payload from the accumulated payload signature. Of course, many other aspects and disclosure are provided in this patent document.

Hierarchical watermark detector

Signal processing devices and methods estimate a geometric transform of an image signal. From a seed set of transform candidates, a method applies a seed transform candidate to a reference signal. 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. 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. The geometric transform is used to facilitate extracting embedded digital messages from the image.

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.

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Tigard, OR, United States of America

Osama M Alattar