Issue No. 09 - September (2011 vol. 33)
Orazio Gallo , University of California, Santa Cruz, Santa Cruz
Roberto Manduchi , University of California, Santa Cruz, Santa Cruz
Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications. For instance, they can enable users to access reviews or price comparisons for a product from a picture of its barcode while still in the store. Barcode reading needs to be robust to challenging conditions such as blur, noise, low resolution, or low-quality camera lenses, all of which are extremely common. Surprisingly, even state-of-the-art barcode reading algorithms fail when some of these factors come into play. One reason resides in the early commitment strategy that virtually all existing algorithms adopt: The image is first binarized and then only the binary data are processed. We propose a new approach to barcode decoding that bypasses binarization. Our technique relies on deformable templates and exploits all of the gray-level information of each pixel. Due to our parameterization of these templates, we can efficiently perform maximum likelihood estimation independently on each digit and enforce spatial coherence in a subsequent step. We show by way of experiments on challenging UPC-A barcode images from five different databases that our approach outperforms competing algorithms. Implemented on a Nokia N95 phone, our algorithm can localize and decode a barcode on a VGA image (640 \times 480, JPEG compressed) in an average time of 400-500 ms.
Barcodes, UPC-A, mobile devices, deformable templates.
R. Manduchi and O. Gallo, "Reading 1D Barcodes with Mobile Phones Using Deformable Templates," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1834-1843, 2010.