Digimarc Corporation

Jacob L Boles

John Douglas Lord

Ravi K Sharma

Alastair M Reed

Osama M Alattar

Robert G Lyons

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

Signal detection and recognition employees coordinated illumination and capture of images under to facilitate extraction of a signal of interest. Pulsed illumination of different colors facilitates extraction of signals from color channels, as well as improved signal to noise ratio by combining signals of different color channels. The successive pulsing of different color illumination appears white to the user, yet facilitates signal detection, even for lower cost monochrome sensors, as in barcode scanning and other automatic identification equipment.

Coordinated illumination and image signal capture for enhanced signal detection

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.

There are many advantages to implementing a watermark-based system using dedicated hardware, rather than using software executing on a general purpose processor. These include higher speed and lower power consumption. However, hardware implementations incur substantial design and development costs. Moreover, because each watermarking application has its own design constraints and parameters, it has not been cost-effective to develop a hardware chip design for each, since such chips would typically not be manufactured in volumes sufficient to bring per-unit costs down to an acceptable level. The present technology provides various techniques for making watermarking hardware adaptable, so that a single chip can serve multiple diverse watermark applications. By so-doing, the advantages of hardware implementation are made available where it was formerly cost-prohibitive, thereby enhancing operation of a great variety of watermark-based systems.

Hardware-adaptable watermark systems

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.

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Portland, OR, United States of America

Jacob L Boles