Quick Response Barcode Research Articles

A QR Code-Based Approach to Differentiating the Display of Augmented Reality Content

The augmented reality (AR) system requires markers to recognize and locate virtual objects on the screens of mobile devices. However, both markers and objects must be registered via the online platform in advance. In addition, an AR marker can only pair with a fixed set of virtual objects, limiting the flexibility and immediacy of changing and updating these data. This paper incorporates the quick response barcode (QR code) into the AR system to address these issues. We propose an algorithm with two vital goals, including (1) generating differentiated virtual objects for different target users by using only one QR code as the marker and (2) concealing different private authentication in QR modules by applying the error correction capability. We then demonstrate the proposed approach via a simulation of two practical scenarios, the electronic catalogs for business applications, and differentiated instructional materials for digital learning. This paper contributes to AR and QR code research and practices.

A REVIEW ON STEGANOGRAPHY AND DATA HIDING IN QR CODE

In comparison to 1D barcodes, a two-dimensional matrix is regarded as a Quick-Response (QR) code which is a programme that contains a vast quantity of data. QR codes offer quick scanning, omnidirectional reading, and a variety of other benefits, such as error correction. Barcode scanners can readily reveal the content of QR codes. In reality, however, gratified QR apps could contain confidential information, like an electronic-ticket or an electronic-coupon. To safeguard quick response essence from unauthorized users or scanners, it should be password protected. We'll look at QR codes and different sorts of QR barcode steganography mechanisms in this article.

Quick response barcode deblurring via doubly convolutional neural network

Various image preprocessing applications for two dimensional (2D) barcode involve reversing the degradation operations (e.g. deblurring). Most of the previously proposed deblurring approaches focus on the construction of suitable deconvolution models, which have shown significant performance at laboratory level. However, the model-based image deblurring solutions might not work well in practical scenarios. To deal with this problem, we propose a convolutional neural network (CNN) based framework to tackle the parameter-free situation for 2D barcode deblurring. The proposed solution leverages the deep learning technique to bridge the gap between traditional model-based methods and requirement of reversing the blurry 2D barcode images. Experiments on practically blurred quick response (QR) barcode images demonstrate that the proposed approach achieves the superior performance in comparison with state-of-the-art model-based image deblurring approaches.

High payload secret hiding technology for QR codes

Quick response (QR) code has become one of the more popular two-dimensional barcodes because of its greater data capacity and higher damage resistance. The barcode scanners can easily extract the information hidden in the QR code while scanning the data modules. However, some sensitive data directly stored in QR codes are insecure in real-world QR applications, such as the e-ticket and e-coupon. To protect the sensitive data, this paper explores the characteristics of QR barcodes to design a secret hiding mechanism for the QR barcode with a higher payload compared to the past ones. For a normal scanner, a browser can only reveal the formal information from the marked QR code. The authorized user/scanner can further reveal the sensitive data from the marked QR tag. The experiments demonstrate a satisfactory secret payload and the feasibility of the proposed scheme.

Anti-counterfeiting digital watermarking algorithm for printed QR barcode

Anti-counterfeiting watermarking technology provides a potential way to prevent quick response (QR) barcode from being copied or forged, and many relevant algorithms for digital QR barcode appeared recently. However, as for printed QR barcode, there is no actual research achievement published. This paper studies anti-counterfeiting techniques for printed QR barcode. The primary performance parameter, decoding rate of QR barcode as well as detection rate of digital watermark, is defined and discussed. A multi-channel robust watermarking scheme based on discrete wavelet transform (DWT) is proposed. In the watermarking scheme, the DWT domain is divided into non-overlapping rectangular areas as called watermarking channels. Channel watermark as well as anti-counterfeiting watermark is embedded into each watermarking channel. At the stage of anti-counterfeiting watermark extracting, the distortion rates of the watermarking channels are estimated by channel watermark. Based on the distortion rates, anti-counterfeiting watermark is computed by a well-designed linear estimation algorithm, whose validity is theoretically proved by analyzing anti-counterfeiting watermark bit error. The experimental results show that the detection rate of anti-counterfeiting watermark is greatly improved with the proposed scheme, and that we can correctly extract anti-counterfeiting watermark from printed QR image by choosing reasonable parameters.

Fragment‐type UHF RFID tag embedded in QR barcode label

The fragment-type appearance of two-dimensional barcodes provides the hint of embedding a fragment-type ultra-high frequency radio frequency identification (UHF RFID) tag into the barcode. In this reported work, genetic algorithm searching is used to select which modules in a quick response (QR) barcode should be metalised and connected to form the RFID tag antenna. The design is demonstrated by embedding a 915 MHz UHF RFID tag in a QR barcode label of dimensions 14.5 × 14.5 mm on a single-sided PCB.

The implementation of embedded quick response codes into library resources to improve service delivery

In late 2010, the Claude Moore Health Sciences Library (CMHSL) launched an experimental implementation of quick response (QR) codes with the goal of determining whether certain services could be delivered more efficiently with this method. A QR code is a “specific matrix barcode (or two-dimensional code) that is readable by dedicated QR barcode readers and camera telephones. The code consists of black modules arranged in a square pattern on a white background. The information encoded may be text, [uniform resource locator] URL, or other data” [1]. Dating back to 1994 when they were created by Japan's Denso-Wave Corporation [2], QR codes have not only migrated into everyday life in many parts of Asia, but global use in advertising is experiencing exponential growth [3]. The 2009 Horizon Report classified QR codes as a key trend in the four-to-five-year category, specifically citing education as a primary area of use [4]. Some libraries, such as the library at the University of Bath, were avid adopters of the technology and utilized QR codes in the library catalog, making it possible to save information about a book in order to locate it on the shelf and to link students to an MP3 audio tour of the library [5]. Ramsden and Jordan write that, despite owning smart phones with the capability to read QR codes, student awareness of the technology remains very low. However, they point out that the potential for the technology to emerge further into student awareness is encouraging [5]. CMHSL believed that younger people would have the most familiarity with QR codes, with students being a prime demographic, and this supposition is supported by a survey conducted by the University of Bath across four different universities [6].

An Effective Recognition Method of Quick Response Barcode in Complex Image Conditions Based on Multi-Constraint Hough Transform

Quick Response (QR) barcodes are being used in a wide variety of applications. But the discrimination rate won’t be high because the collected bar codes (form a camera or scanner) are in various imaging conditions. Obtaining and correcting the QR barcode inclination angle is the basis of the QR barcode recognition technique. In this paper, using the characteristics of QR barcodes, we propose to recognize the QR barcodes in complex image conditions by multi-constraint Hough transform method, which combines the inclination angle constraint and edge-point constraint to obtain and correct the QR barcode inclination angle efficiently. The proposed method was evaluated on three data sets. The experimental results indicate that the transform time of our method is 0.3% of the transform time of the classic Hough transform. In particular, the proposed method provides a valuable QR barcode recognition solution in complex image conditions.

Bird's singing codification using quick response barcode

This work responds to the necessity to objectively describe bird's songs in scientific publications and field guides. Nevertheless, it can also be extended to the study of other animal like marine mammals, bats, humans, and insects. The project consists on a computer application development in MAX/MSP language, that extracts coefficients from an audio signal, those coefficients will characterize the signal, and are codified by QR tool (Quick Response Barcode), created by the Japanese company Denso-Wave in 1994. The following stage of the project, that is in developing process, is the programming of the de-codifier application, that rebuild the audio signal from those numbers, to bring back the signal to the human hearing field, as final stage of the project, it will be developed a similar application with the same intentions for mobile phones with the built-in QR reading tool.