{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:35:18Z","timestamp":1761676518558,"version":"3.37.3"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["EU FP7 PEOPLE IRSES"],"award-info":[{"award-number":["EU FP7 PEOPLE IRSES"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%\u2013100% with a speed compatible to real-time applications. In particular, speed varies from 200\u2009ms to 800\u2009ms per single or multiple QR code detected simultaneously in images with resolution from 640 \u00d7 480 to 4080 \u00d7 2720, respectively.<\/jats:p>","DOI":"10.1155\/2017\/4613628","type":"journal-article","created":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T17:02:19Z","timestamp":1501088539000},"page":"1-15","source":"Crossref","is-referenced-by-count":7,"title":["Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments"],"prefix":"10.1155","volume":"2017","author":[{"given":"Omar","family":"Lopez-Rincon","sequence":"first","affiliation":[{"name":"Department of Computing, Electronics and Mechatronics, University de las Americas Puebla, 72810 San Andr\u00e9s Cholula, PUE, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9763-7651","authenticated-orcid":true,"given":"Oleg","family":"Starostenko","sequence":"additional","affiliation":[{"name":"Department of Computing, Electronics and 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