Feature-based watermark localization in digital capture systems

The “Internet of Things” is an appealing concept aiming to assign digital identity to both physical and digital everyday objects. One way of achieving this goal is to embed the identity in the object itself by using digital watermarking. In the case of printed physical objects, such as consumer packages, this identity can be later read from a digital image of the watermarked object taken by a camera. In many cases, the object might occupy only a small portion of the the image and an attempt to read the watermark payload from the whole image can lead to unnecessary processing. This paper proposes a statistical learning-based algorithm for localizing watermarked physical objects taken by a digital camera. The algorithm is specifically designed and tested on watermarked consumer packages read by an off-the-shelf barcode imaging scanner. By employing simple noise-sensitive features borrowed from blind image steganalysis and a linear classifier, we are able to estimate probabilities of watermark presence in every part of the image significantly faster than running a watermark detector. These probabilities are used to pinpoint areas that are recommended for further processing. We compare our adaptive approach with a system designed to read watermarks from a set of fixed locations and achieve significant savings in processing time while improving overall detector robustness.