A symbol-based algorithm for decoding bar codes

We investigate the problem of decoding a bar code from a signal measured with a hand-held laser-based scanner. Rather than formulating the inverse problem as one of binary image reconstruction, we instead incorporate the symbology of the bar code into the reconstruction algorithm directly, and search for a sparse representation of the UPC bar code with respect to this known dictionary. Our approach significantly reduces the degrees of freedom in the problem, allowing for accurate reconstruction that is robust to noise and unknown parameters in the scanning device. We propose a greedy reconstruction algorithm and provide robust reconstruction guarantees. Numerical examples illustrate the insensitivity of our symbology-based reconstruction to both imprecise model parameters and noise on the scanned measurements.Comment: 24 pages, 12 figure

Reconciling Bayesian and Total Variation Methods for Binary Inversion

A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization. On the other hand, sparse or noisy data often demands a probabilistic approach to the reconstruction of images, to enable uncertainty quantification; the Bayesian approach to inversion is a natural framework in which to carry this out. The link between Bayesian inversion methods and perimeter regularization, however, is not fully understood. In this paper two links are studied: (i) the MAP objective function of a suitably chosen phase-field Bayesian approach is shown to be closely related to a least squares plus perimeter regularization objective; (ii) sample paths of a suitably chosen Bayesian level set formulation are shown to possess finite perimeter and to have the ability to learn about the true perimeter. Furthermore, the level set approach is shown to lead to faster algorithms for uncertainty quantification than the phase field approach

A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes

QR bar codes are prototypical images for which part of the image is a priori known (required patterns). Open source bar code readers, such as ZBar, are readily available. We exploit both these facts to provide and assess purely regularization-based methods for blind deblurring of QR bar codes in the presence of noise.Comment: 14 pages, 19 figures (with a total of 57 subfigures), 1 table; v3: previously missing reference [35] adde

Image pre-processing to improve data matrix barcode read rates

The main goal of this study is to research image processing methods in attempts to develop a robust approach to image pre-preprocessing of Data Matrix barcode images that will improve barcode read rates in an open source fashion. This is demonstrated by element state classification to re-create the ideal binary matrix corresponding to the intended barcode layout through pattern recognition theory. The research consisted of implementing and evaluating the effectiveness of many image processing algorithms types, as well as evaluating key features that clearly delineate different element states. The algorithms developed highlight the use of morphological erosion and region growing for object segmentation and edge analysis and Fisher\u27s Linear Discriminant as a means for element classification. The results demonstrate successful barcode binarization for ideal barcodes with improved read rates in most cases. The techniques developed here provide ground work for a test bed environment to continue improvements by analyzing non-ideal barcodes for additional robustness