Digimarc Corporation

Eliot Rogers

Tony F Rodriguez

Geoffrey B Rhoads

Alastair M Reed

Adnan M Alattar

Bruce L Davis

Ravi K Sharma

Edward Bruce Knudson

William Y Conwell

Hugh L Brunk

Brett Alan Bradley

Brett T Hannigan

John Douglas Lord

Yang Bai

Brian T MacIntosh

Scott M Long

Kurt M Eaton

Robert Craig Brandis

Colin P Cornaby

Jerrine K Owen

Matthew M Weaver

Joshua V Aller

Utkarsh Deshmukh

Eoin C Sinclair

Gilbert B Shaw

Emma C Sinclair

Arrangements involving portable devices (e.g., smartphones and tablet computers) are disclosed. One arrangement enables a content creator to select software with which that creator's content should be rendered&#x2014;assuring continuity between artistic intention and delivery. Another utilizes a device camera to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some technologies concern improvements to the user interfaces associated with such devices. For example, some arrangements enable discovery of both audio and visual content, without any user requirement to switch modes. Other technologies involve use of these devices in connection with shopping, text entry, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern computational photography. A great variety of other features and arrangements are also detailed.

Smartphone-based methods and systems

The present disclosure relates to signal decoding and icon (e.g., a logo, shape, icon, etc.) detection. In some implementations, a first response is provided upon signal decoding but no icon detection within first captured image data, and a second response is provided upon signal decoding and icon detection within the first captured image data. Other aspects, combinations and implementations are described as well.

Detection of encoded signals and icons

Arrangements involving portable devices (e.g., smartphones and tablet computers) are disclosed. One arrangement enables a content creator to select software with which that creator's content should be rendered&#x2014;assuring continuity between artistic intention and delivery. Another utilizes a device camera to identify nearby subjects, and take actions based thereon. Others rely on near field chip (RFID) identification of objects, or on identification of audio streams (e.g., music, voice). Some technologies concern improvements to the user interfaces associated with such devices. Others involve use of these devices in connection with shopping, text entry, sign language interpretation, and vision-based discovery. Still other improvements are architectural in nature, e.g., relating to evidence-based state machines, and blackboard systems. Yet other technologies concern use of linked data in portable devices&#x2014;some of which exploit GPU capabilities. Still other technologies concern computational photography. A great variety of other features and arrangements are also detailed.

Methods and arrangements involving portable user devices such smartphones and wearable electronic devices are disclosed, as well as other devices and sensors distributed within an ambient environment. Some arrangements enable a user to perform an object recognition process in a computationally- and time-efficient manner. Other arrangements enable users and other entities to, either individually or cooperatively, register or enroll physical objects into one or more object registries on which an object recognition process can be performed. Still other arrangements enable users and other entities to, either individually or cooperatively, associate registered or enrolled objects with one or more items of metadata. A great variety of other features and arrangements are also detailed.

Methods for object recognition and related arrangements

One arrangement concerns a portable device (e.g., a smartphone) that executes plural recognition agents, such as agents that perform fingerprint-based object recognition, fingerprint-based audio recognition, barcode reading, watermark decoding, etc. Each of the agents reads from and writes to a blackboard data structure, to which camera and microphone sensors also post their data. Queues of stored sensor data are thus available for the agents to process. In some arrangements, the agents also post&#x2014;to the blackboard&#x2014;estimates of the resource costs required to perform certain functions, and estimates of the quality of results that may be achieved by such functions. This allows the system to make informed decisions about how to deploy the device's limited resources (battery, processing cycles, network bandwidth, etc.). A great variety of other features and arrangements are also detailed.

Device, system and method for recognizing inputted data using memory having blackboard data structure

Variable processing of both image and audio data based on processor utilization

This document relates generally to digital watermarking. One claim recites a method including: obtaining data representing visual media; determining a model through iteratively embedding a digital watermark signal in the data; and embedding the digital watermark signal in the data according to the model. The model maximizes robustness of the digital watermark signal while minimizing perceptibility of the digital watermark signal when the signal is embedded in the data. A new embedding form or embedding mask is created after each embedding iteration. Of course, other combinations are described and claimed as well.

Perceptability model applied to watermark signals

A digital watermarking system uses a model of a rendering device to take into account the impact of the rendering device on visual or auditory quality of the watermarked signal. For visual content, a reference image and watermarked image are passed through the model and then supplied to a visual quality metric that assesses the visibility of the watermark. The digital watermark embedding is adjusted iteratively with each pass through this process to produce a watermarked signal that has the desired visual quality.

Human perceptual model applied to rendering of watermarked signals

The present invention provides a mechanism for automatically setting the exposure time and gain of an electronic camera so that the acquired image has improved characteristics for the detection of a digital watermark. With the present invention the exposure time and gain of an electronic camera are set by first directing the camera at a test target with the camera's controls set to a default set of values. The system then calculates a histogram of the luminance values of the pixels in the image. Next characteristic values from the histogram such as the peak value, the centroid value, the RMS value, the median value, etc. are calculated. One or more of the characteristic values of the histogram are then applied as an index or pointer to values of pre-established settings that are stored in a table. Values from the table are used to set the exposure time and/or gain of the camera. In some embodiments, the variance of the luminance histogram is also measured and used to index data from the table.

Adjusting an electronic camera to acquire an image

Adjusting an electronic camera to acquire a watermarked image

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Beaverton, OR, United States of America

Eliot Rogers