Automatikus azonosítás és hitelesítés vizuális kódokkal

Az automatikus azonosítás egyik legfontosabb és széles körben alkalmazott eleme a vizuális kódokkal történő azonosítás. A különbözô szimbólumokkal és mintákkal megjelenített azonosítók teszik lehetôvé a gépek számára az elektronikus leolvasást, ami nagyban segíti és gyorsítja a feldolgozást pl. a bolti pénztáraknál, raktári átvételnél, nagy sebességű feldolgozási helyeken, gyártósorokon. A szokásosan használt, geometriai minták szerint tervezett kódok általában típusokat vagy egyedeket azonosítanak. Elôállíthatók azonban olyan mintázatok, melyek természetüknél fogva egyediek és így eredetiség vagy hitelesség ellenôrzésére is alkalmazhatók. Jelen írásunkban bemutatunk egy módszert QR kódok gyors és pontos detektálására mobil készülékkel készített fényképeken, valamint egy természetes mintázat felismerésére kidolgozott eljárásunkat. Alkalmazási területként bemutatunk egy olyan lehetséges hibrid vizuális kód konstrukciót, melyben mesterséges és természetes mintázatok együttes alkalmazásával elérhetô az azonosítás és a hitelesítés is

Rape myth acceptance as a relevant psychological construct in a gender-unequal context: The Hungarian adaptation of the updated Illinois rape myths acceptance scale

The Updated Illinois Rape Myth Acceptance Scale (UIRMAS) has been widely used for measuring rape myth acceptance. The scale was created in the United States, however studies have shown that rape myth is a culturally and socially embedded phenomenon. Therefore, in order to measure rape myth acceptance in other parts of the world, the scale needs to be validated. Victim blaming and rape myths are both widespread in public reactions to rape in Hungary (ie, in media reports and public opinion). Furthermore, Hungary can be characterized by a weak feminist movement and scoring low on gender equality measures. Nevertheless, we expected and found the reliability and validity of the Hungarian version of the Updated Illinois Rape myth acceptance Scale (UIRMAS). In Study 1 we conducted a confirmative factor analysis to assess the structural validity of the scale and identified the original factors of

Efficient 1D and 2D barcode detection using mathematical morphology

Barcode technology is essential in automatic identification, and is used in a wide range of real-time applications. Different code types and applications impose special problems, so there is a continuous need for solutions with improved performance. Several methods exist for code localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need reliable automatic barcode localization, e.g. conveyor belts and automated production, where missed detections cause loss of profit. Our goal is to detect automatically, rapidly and accurately the barcode location with the help of extracted image features. We propose a new algorithm variant, that outperforms in both accuracy and efficiency other detectors found in the literature using similar ideas, and also improves on the detection performance in detecting 2D codes compared to our previous algorithm

Improved QR code localization using boosted cascade of weak classifiers

Usage of computer-readable visual codes became common in our everyday life at industrial environments and private use. The reading process of visual codes consists of two tasks: localization and data decoding. Unsupervised localization is desirable at industrial setups and for visually impaired people. This paper examines localization efficiency of cascade classifiers using Haar-like features, Local Binary Patterns and Histograms of Oriented Gradients, trained for the finder patterns of QR codes and for the whole code region as well, and proposes improvements in post-processing

A Novel Method for Barcode Localization in Image Domain

Barcode localization is an essential step of the barcode reading process. For industrial environments, having high-resolution cameras and eventful scenarios, fast and reliable localization is crucial. Images acquired in those setups have limited parameters, however, they vary at each application. In earlier works we have already presented various barcode features to track for localization process. In this paper, we present a novel approach for fast barcode localization using a limited set of pixels in image domain

Distance transform and template matching based methods for localization of barcodes and QR codes

Visual codes play an important role in automatic identification, which became an inseparable part of industrial processes. Thanks to the revolution of smartphones and telecommunication, it also becomes more and more popular in everyday life, containing embedded web addresses or other small informative texts. While barcode reading is straightforward in images having optimal parameters (fo cus, illumination, code orientation, and position), localization of code regions is still challenging in many scenarios. Every setup has its own characteristics, there fore many approaches are justifiable. Industrial applications are likely to have more fixed parameters like illumination, camera type and code size, and processing speed and accuracy are the most important requirements. In everyday use, like with smart phone cameras, a wide variety of code types, sizes, noise levels and blurring can be observed, but the processing speed is often not crucial, and the image acquisition process can be repeated in order for successful detection. In this paper, we address this problem with two novel methods for localization of 1D barcodes based on template matching and distance transformation, and a third method for QR codes. Our proposed approaches can simultaneously localize sev eral different types of codes. We compare the effectiveness of the proposed methods with several approaches from the literature using public databases and a large set of synthetic images as a benchmark. The evaluation shows that the proposed methods are efficient, having 84.3% Jaccard accuracy, superior to other approaches. One of the presented approaches is an improvement on our previous work. Our template matching based method is computationally more complex, however, it can be adapted to specific code types producing high accuracy. The other method uses distance transformation, which is fast and gives rough regions of interests that can contain valid visual code candidates