Digimarc Corporation

Other

Digimare Corporation

Digimarc Coporation

Tony F Rodriguez

Geoffrey B Rhoads

Bruce L Davis

Alastair M Reed

Ravi K Sharma

William Y Conwell

Matthew M Weaver

Trent J Brundage

Sean Calhoon

Donald Haaga

Tomas Filler

John F Stach

Brett T Hannigan

Marc D Miller

Brett Alan Bradley

John Douglas Lord

Yang Bai

Eliot Rogers

Kristyn R Falkenstern

Shankar Thagadur Shivappa

Brian T MacIntosh

Kenneth Lee Levy

J Scott Carr

Scott M Long

Hugh L Brunk

Tyler J McKinley

Eric D Evans

Gilbert B Shaw

Rebecca L Gerlach

Edward Bruce Knudson

Vojtech Holub

Adnan M Alattar

Hoyet Harrison Andrews, Iii

Joel R Meyer

Arthur L Powell

Robert Craig Brandis

Stuart Filip Rosove

Osama M Alattar

Kurt M Eaton

Emma C Sinclair

Steven Michael Shovoly

Ajith Mulki Kamath

Ammon E Gustafson

Phillip Andrew Seder

William C Hein, Iii

Joshua V Aller

Clayton L Davidson

Aruna B Kumar

Reed R Stager

Colin P Cornaby

Jerrine K Owen

Mark E Haynes

Kirstin Hierholzer

Jorge L Garcia

Burt W Perry

Jeremy Leigh Cattone

Andrew Evan Klonsky

Stephen K Decker

Vahid Sedighianaraki

Mark-Andrew Ray Tait

Kevin C Jones

Eric E Ellingson

Steven W Stewart

David A Cushman

Nicole Rhoads

R Stephen Hiatt

Stephen A Lauer

Jonathan Scott Carr

Marc G Footen

Ernest Medina

Donald L Haaga, Jr

Richard F Gibson

Brian Mosher

Cynthia K Aggson

Nikolaus Stuecklen

Brian T Macintosh

John Stach

Hoyet Harrison Iii Andrews

This disclosure details image and audio signal processing methods and associated equipment to robustly encode transaction parameters in rendered displays, printed objects and audio. It also details corresponding decoding methods and equipment to recover these parameters. Further, it details object authentication processing and equipment to validate a transaction for an object, employing a trust network protocol for maintaining a trusted transaction history of the object. Various alternative forms of this technology are described.

Robust encoding of machine readable information in host objects and biometrics, and associated decoding and authentication

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 steganographic digital watermark signal is decoded from host imagery without requiring a domain transformation for signal synchronization, thereby speeding and simplifying the decoding operation. In time-limited applications, such as in supermarket point-of-sale scanners that attempt watermark decode operations on dozens of video frames every second, the speed improvement allows a greater percentage of each image frame to be analyzed for watermark data. In battery-powered mobile devices, avoidance of repeated domain transformations extends battery life. A great variety of other features and arrangements, including machine learning aspects, are also detailed.

Image-based pose determination

In a next-generation retail store, the presence of a shopper at a particular shelf location, at about the time that an item was removed from that shelf location, gives rise to a hypothesis that the item may have been picked for purchase by that shopper. An ensemble of different such hypotheses, about different items removed by different shoppers, is evaluated jointly to determine which hypotheses are most likely. Other factors can influence the determinations, such as a physical distance between picked products, together with a time interval between their picking. If cameras are used to detect removal of items from shelves, hand depictions captured by such cameras are further factors that may be considered, e.g., helping associate certain item removals with a common shopper. By generating purchase lists for each shopper with such methods, shoppers can be relieved of the need of having each item scanned at a checkout. Some embodiments are privacy-preserving—neither employing nor capturing personally-identifying information. Other embodiments don't require tracking movements of shoppers through the store. A great number of other features and arrangements are also detailed.

Sensor-based maximum-likelihood estimation of item assignments

Variable data printing workflows are enhanced for use with content that includes 2D code patterns, such as digital watermark data. One arrangement includes applying a filter to a content stream within a PDF document to extract both first variable pattern data for a first watermark pattern and second variable pattern data for a second watermark pattern. A first composite watermark pattern is then defined based on the extracted first variable watermark pattern data in conjunction with static watermark pattern data, and a second composite watermark pattern is defined based on the extracted second variable watermark pattern data in conjunction with the static watermark pattern data. A variety of other features and arrangements are also detailed.

Serialized digital watermarking for variable data printing

Items are identified in a waste stream for purposes of recycling, using deterministic and/or probabilistic techniques. Imagery of the waste stream from multiple viewpoints permit creation of a 3D depth draped image representation, from which one or more 2D planes can be synthesized. Phase-coherent patches of recoverable encoded data can be identified from among soiled and crumpled object surfaces, and used in combination to recover object identification information. Recognition of certain items can trigger further image processing that is specific to such items. (Detection of a catsup bottle, for example, can trigger image analysis to discern the presence of catsup residue.) Information about recognized objects can be provided to external data customers, e.g., to track grey market diversion of particular products into unlicensed territories. These and other features and advantages, which can be used alone or in combination, are detailed herein.

Information-client server built on a rapid material identification platform

A decade from now, a visit to the supermarket will be a very different experience than the familiar experiences of decades past. Product packaging will come alive with interactivity—each object a portal into a rich tapestry of experiences, with contributions authored by the product brand, by the store selling the product, and by other shoppers. The present technology concerns arrangements for authoring and delivering such experiences. A great variety of other features and technologies are also detailed.

Imagery and annotations

Digital watermarking is adapted for the variable data printing. A reference signal serves as a proxy for optimizing the embedding a watermark in a host image to be printed. Using the reference signal, embedding parameters are generated, which are a function of constraints such as visual quality and robustness of the machine readable data. Adjustments needed to embed a unique payload in each printed piece are generated using the embedding parameters. These adjustments are stored in a manner that enables them to be efficiently obtained and applied within the RIP or press during operation of the press. Various other methods, system configurations and applications are also detailed.

Deterministic identifiers fuel reliable efficient capture of product discovery, purchase and consumption events, which in turn enable more reliable product recommendation, more accurate shopping list generation and in-store navigation. A mobile device, equipped with image and audio detectors, extracts product identifiers from objects, display screens and ambient audio. In conjunction with a cloud-based service, a mobile device application obtains product information and logs product events for extracted identifiers. The cloud service generates recommendations, and mapping for in-store navigation. The detectors also provide reliable and efficient product identification for purchase events, and post shopping product consumption events.

Object identification and device communication through image and audio signals

A store includes first and second racks of shelving that are respectively viewed by first and second cameras. A computer system, including one or more processors and memory, defines a first convolutional neural network (CNN) trained to recognize a first set of items stocked on the first rack of shelving, and a second CNN trained to recognize a second set of items stocked on the second rack of shelving. (The first camera provides imagery for recognition by the first CNN, and the second camera provides imagery for recognition by the second CNN.) In such arrangement, the second neural network is trained to recognize a particular item that the first neural network is not trained to recognize, and the first neural network is trained to recognize a certain item that the second neural network is not trained to recognize. Many other features and arrangements are also detailed.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed views&#x2014;further minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and used&#x2014;or ignored&#x2014;in product identification. A great variety of other features and arrangements are also detailed.

Methods and arrangements for identifying objects

Images depicting items in a waste flow on a conveyor belt are provided to two analysis systems. The first system processes images to decode digital watermark payload data found on certain of the items (e.g., plastic containers). This payload data is used to look up corresponding attribute metadata for the items in a database, such as the type of plastic in each item, and whether the item was used as a food container or not. The second analysis system can be a spectroscopy system that determines the type of plastic in each item by its absorption characteristics. When the two systems conflict in identifying the plastic type, a sorting logic processor applies a rule set to arbitrate the conflict and determine which plastic type is most likely. The item is then sorted into one of several different bins depending on a combination of the final plastic identification, and whether the item was used as a food container or not. A variety of other features and arrangements are also detailed.

Arrangements for digital marking and reading of items, useful in recycling

The present disclosure relates to signal processing such as image processing, signal encoding, digital watermarking and data hiding. One claim recites a method including: capturing imagery corresponding to a printed object with a red or blue illumination source, the red or blue illumination source having an illumination wavelength at or around 660 nm or an illumination wavelength in the range of 60 nm centered around 460 nm, said capturing imagery yielding captured data; wherein the printed object includes a clear coat printed thereon, the clear coat including a material that has a peak absorbance at or around 660 nm or in the range of 60 nm centered around 460 nm, the clear coat printed in a manner to convey an encoded plural-bit message, the encoded plural-bit message corresponding to product information; analyzing the captured data with one or more programmed multi-core processors to decode the encoded plural-bit message, said analyzing yielding the product information; and providing the product information as an output. Of course, other claims, technology and combinations are disclosed.

Digital watermarking and data hiding with clear topcoats

A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

Growing community of inventors

Portland, OR, United States of America

Tony F Rodriguez