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Vision-Based Localization and Scanning of 1D UPC and EAN Barcodes with Relaxed Pitch, Roll, and Yaw Camera Alignment Constraints
Vladimir Kulyukin, Tanwir Zaman
Pages - 355 - 383     |    Revised - 10-07-2014     |    Published - 15-09-2014
Volume - 8   Issue - 5    |    Publication Date - September / October 2014  Table of Contents
Skewed Barcode Localization & Scanning, Image Gradients, Mobile Computing, Eyes-free Computing, Cloud Computing.
Two algorithms are presented for vision-based localization of 1D UPC and EAN barcodes with relaxed pitch, roll, and yaw camera alignment constraints. The first algorithm localizes barcodes in images by computing dominant orientations of gradients (DOGs) of image segments and grouping smaller segments with similar DOGs into larger connected components. Connected components that pass given morphological criteria are marked as potential barcodes. The second algorithm localizes barcodes by growing edge alignment trees (EATs) on binary images with detected edges. EATs of certain sizes mark regions as potential barcodes. The algorithms are implemented in a distributed, cloud-based system. The system’s front end is a smartphone application that runs on Android smartphones with Android 4.2 or higher. The system’s back end is deployed on a five node Linux cluster where images are processed. Both algorithms were evaluated on a corpus of 7,545 images extracted from 506 videos of bags, bottles, boxes, and cans in a supermarket. All videos were recorded with an Android 4.2 Google Galaxy Nexus smartphone. The DOG algorithm was experimentally found to outperform the EAT algorithm and was subsequently coupled to our in-place scanner for 1D UPC and EAN barcodes. The scanner receives from the DOG algorithm the rectangular planar dimensions of a connected component and the component’s dominant gradient orientation angle referred to as the skew angle. The scanner draws several scanlines at that skew angle within the component to recognize the barcode in place without any rotations. The scanner coupled to the localizer was tested on the same corpus of 7,545 images. Laboratory experiments indicate that the system can localize and scan barcodes of any orientation in the yaw plane, of up to 73.28 degrees in the pitch plane, and of up to 55.5 degrees in the roll plane. The videos have been made public for all interested research communities to replicate our findings or to use them in their own research. The front end Android application is available for free download at Google Play under the title of NutriGlass.
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Dr. Vladimir Kulyukin
Utah State University - United States of America
Mr. Tanwir Zaman
Utah State University - United States of America