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Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.080924
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 9, 2017.
Abstract: The widespread utilization of QR code and its coincidence with the swift growth of e-commerce transactions have imposed the computer vision researchers to continuously devise a variety of QR code recognition algorithms. The latter performances are generally limited due to two main factors. Firstly, most of them are computationally expensive because of the implemented feature descriptor complexities. Secondly, the evoked algorithms are often sensitive to pattern geometric deformations. In this paper a robust approach is proposed, in which the architecture is based on three distinct treatments among others: 1) An image quality assessment stage which evaluates the quality of the captured image in consideration that the presence of blur decreases significantly the recognition accuracy. 2) This stage is followed by an image segmentation based on an achromatic filter through which only the regions of interest are highlighted and consequently the execution time is reduced. 3) Finally, the Hu invariant moments technique is used as feature descriptor permitting removing false positives. This technique is implemented to filter out the set of extracted candidate QR code patterns, which have been roughly extracted by a scanning process. The Hu moments descriptor is able to recognize patterns independently of the geometric transformations they undergo. The experiments show that the incorporation of the aforementioned three stages enhances significantly the recognition accuracy along with a notable diminution of processing time. This makes the proposed approach adapted to embedded systems and devices with limited performances.
Hicham Tribak and Youssef Zaz, “QR Code Patterns Localization based on Hu Invariant Moments” International Journal of Advanced Computer Science and Applications(IJACSA), 8(9), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080924