A video processing method for convenient mobile reading of printed barcodes with camera phones

Abstract

Efficient communication requires an appropriate choice and combination of media. The print media has succeeded to attract audiences also in our electronic age because of its high usability. However, the limitations of print are self evident. By finding ways of combining printed and electronic information into so called hybrid media, the strengths of both media can be obtained. In hybrid media, paper functions as an interface to the web, integrating printed products into the connected digital world. This is a "reinvention" of printed matter making it into a more communicative technology. Hybrid media means that printed products can be updated in real time. Multimedia clips, personalization and e-shopping can be added as a part of the interactive medium.The concept of enhancing print with interactive features has been around for years. However, the technology has been so far too restricting -people don't want to be tied in front of their PC's reading newspapers. Our solution is communicative and totally mobile. A code on paper or electronic media constitutes the link to mobility.

Experiences from a Touch-Based Interaction and Digitally Enhanced Meal-Delivery Service for the Elderly

“…As mobile phones are so common, becoming almost ubiquitous, it is lucrative to combine the tag reading functionality with the mobile phone. Modern mobile phones often have camera capability which enables detecting and processing visual tags [4,5]. Also, mobile phones with RFIDbased readers are available [6].…”

Knowledge guided adaptive binarization for 2D barcode images captured by mobile phones

“…Thereafter, it is binarized using a global threshold, in which the good quality of the binarized image cannot be guaranteed. Simply using the mean value as global [3] or local [4] thresholds does not work well for barcode images. Liu et al propose to use the clustering technique to binarize the barcode images [5], which may require more computing power.…”

Accurate localization of four extreme corners for barcode images captured by mobile phones

“…Using eight angular lines to scan the barcode images and taking the intersection point that lie near to the origin as the corner location is sensitive to noise and the geometric transformation that the barcode image has gone through [2]. Similarly, using the minimum and maximum to detect the corner points is sensitive to the geometric distortions occured to the barcode images [3]. A two-stage scheme for locating the 1D barcode images is discussed in [4].…”