Abstract:
Sparse signal modulation schemes encode a data channel on a host image in a manner that is robust, flexible to achieve perceptual quality constraints, and provides improved data capacity. The host image is printed by any of a variety of means to apply the image, with sparse signal, to an object. After image capture of the object, a decoder processes the captured image to detect and extract data modulated into the sparse signal. The sparse signal may incorporate implicit or explicit synchronization components, which are either formed from the data signal or are complementary to it.
Abstract:
The present disclosure relates to advanced signal processing including digital watermarking and steganography. One method includes: obtaining image data representing imagery in a first color mode; steganographically embedding a watermark signal into the image data, thereby generating watermarked image data; obtaining reader data representing an input color mode of a reader configured to detect or decode the watermarked image data, wherein the input color mode is different from the first color mode; transforming the watermarked image data from the first color mode to the input color mode; estimating a signal strength of the watermark signal within the transformed watermark image data; and visually displaying the estimated signal strength. The estimated signal strength can be visually displayed as a multi-color heat map. Of course, other combinations are described as well.
Abstract:
The present disclosures relates generally to digital watermarking and data hiding. One claim recites a method comprising: obtaining data representing captured imagery, the captured imagery depicting packaging including digital watermarking, the digital watermarking including an orientation signal that is detectable in a transform domain; generating a n-dimensional feature set of the data representing captured imagery, the n-dimensional feature set representing the captured imagery in a spatial domain, where n is an integer great than 13; using a trained classifier to predict the presence of the orientation signal in a transform domain from the feature set in the spatial domain. Of course, other claims and combinations are provided too.