00547nas a2200133 4500008004100000245007600041210006900117260004800186300001000234490000700244100002000251700002300271856011900294 2015 eng d00aImproved QR Code Localization Using Boosted Cascade of Weak Classifiers0 aImproved QR Code Localization Using Boosted Cascade of Weak Clas aSzeged, HungarybUniversity of Szegedc2015 a21-330 v221 aBodnár, Péter1 aNyúl, László, G uhttps://www.inf.u-szeged.hu/en/publication/improved-qr-code-localization-using-boosted-cascade-of-weak-classifiers00572nas a2200157 4500008004100000245006200041210006200103260004400165300001200209100002000221700002800241700001200269700001600281700001200297856010500309 2015 eng d00aLocalization of Visual Codes using Fuzzy Inference System0 aLocalization of Visual Codes using Fuzzy Inference System aBerlin, GermanybSciTePresscMarch 2015 a345-3521 aBodnár, Péter1 aNyúl, László, Gábor1 aBraz, J1 aBattiato, S1 aImai, F uhttps://www.inf.u-szeged.hu/en/publication/localization-of-visual-codes-using-fuzzy-inference-system00523nas a2200121 4500008004100000245007500041210007500116260004000191300001200231100002000243700002800263856011000291 2015 hun d00aQR kód lokalizáció kaszkádolt gyenge osztályozók használatával0 aQR kód lokalizáció kaszkádolt gyenge osztályozók használatával aKecskemét, MagyarországcJan 2015 a712-7211 aBodnár, Péter1 aNyúl, László, Gábor uhttps://www.inf.u-szeged.hu/en/publication/qr-kod-lokalizacio-kaszkadolt-gyenge-osztalyozok-hasznalataval00624nas a2200145 4500008004100000245009400041210007900135260004000214300001200254100002000266700002800286700001900314700002000333856012500353 2015 hun d00aVizuális kódok lokalizációja mély egyenirányított neurális háló használatával0 aVizuális kódok lokalizációja mély egyenirányított neurális háló aKecskemét, MagyarországcJan 2015 a546-5611 aBodnár, Péter1 aNyúl, László, Gábor1 aGrósz, Tamás1 aTóth, László uhttps://www.inf.u-szeged.hu/en/publication/vizualis-kodok-lokalizacioja-mely-egyeniranyitott-neuralis-halo-hasznalataval00703nas a2200181 4500008004100000245008600041210006900127260003100196100002000227700001900247700002000266700002800286700002000314700001900334700001900353700002000372856012900392 2014 eng d00aLocalization of Visual Codes in the DCT Domain Using Deep Rectier Neural Networks0 aLocalization of Visual Codes in the DCT Domain Using Deep Rectie aSetúbalbSCITEPRESSc20141 aBodnár, Péter1 aGrósz, Tamás1 aTóth, László1 aNyúl, László, Gábor1 aFilipe, Joaquim1 aGusikhin, Oleg1 aMadani, Kurosh1 aSasiadek, Jurek uhttps://www.inf.u-szeged.hu/en/publication/localization-of-visual-codes-in-the-dct-domain-using-deep-rectier-neural-networks00535nas a2200121 4500008004100000245006700041210006700108260007000175300001000245100002000255700002800275856011000303 2014 eng d00aQR Code Localization Using Boosted Cascade of Weak Classifiers0 aQR Code Localization Using Boosted Cascade of Weak Classifiers aSzegedbInstitute of Informatics, University of SzegedcJune 2014 a6 - 71 aBodnár, Péter1 aNyúl, László, Gábor uhttps://www.inf.u-szeged.hu/en/publication/qr-code-localization-using-boosted-cascade-of-weak-classifiers01169nas a2200145 4500008004100000245006700041210006700108260005000175520059600225100002000821700002800841700001900869700002300888856011200911 2014 eng d00aQR Code Localization Using Boosted Cascade of Weak Classifiers0 aQR Code Localization Using Boosted Cascade of Weak Classifiers aVilamura, PortugalbSpringer-VerlagcOct 20143 a
Usage of computer-readable visual codes became common in oureveryday life at industrial environments and private use. The reading process of visual codes consists of two steps: 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.
1 aBodnár, Péter1 aNyúl, László, Gábor1 aKamel, Mohamed1 aCampilho, Aurélio uhttps://www.inf.u-szeged.hu/en/publication/qr-code-localization-using-boosted-cascade-of-weak-classifiers-000654nas a2200193 4500008004100000245005200041210005200093260004400145100001900189700002000208700002000228700002800248700001900276700001800295700001700313700001600330700001900346856009500365 2014 eng d00aQR code localization using deep neural networks0 aQR code localization using deep neural networks aSep 2014, Reims, FrancebIEEEcSep 20141 aGrósz, Tamás1 aBodnár, Péter1 aTóth, László1 aNyúl, László, Gábor1 aMamadou, Mboup1 aTülay, Adali1 aMoreau, Eric1 aLarsen, Jan1 aGuelton, Kevin uhttps://www.inf.u-szeged.hu/en/publication/qr-code-localization-using-deep-neural-networks01099nas a2200157 4500008004100000020001400041245008400055210006900139260000900208300001200217490000700229520053200236100002000768700002800788856012500816 2013 eng d a0324-721X00aBarcode detection using local analysis, mathematical morphology, and clustering0 aBarcode detection using local analysis mathematical morphology a c2013 a21 - 350 v213 aBarcode detection is required in a wide range of real-lifeapplications. Imaging conditions and techniques vary considerably and each application has its own requirements for detection speed and accuracy. In our earlier works we built barcode detectors using morphological operations and uniform partitioning with several approaches and showed their behaviour on a set of test images. In this work, we extend those ideas with clustering, contrast measuring, distance transformation and probabilistic Hough transformation.
1 aBodnár, Péter1 aNyúl, László, Gábor uhttps://www.inf.u-szeged.hu/en/publication/barcode-detection-using-local-analysis-mathematical-morphology-and-clustering01045nas a2200145 4500008004100000245007600041210006900117260005400186300001200240520052600252100002000778700002800798700001400826856005900840 2013 eng d00aBarcode detection with uniform partitioning and distance transformation0 aBarcode detection with uniform partitioning and distance transfo aInnsbruck, AustriabIASTED - Acta PresscFeb 2013 a48 - 533 aBarcode detection is required in a wide range of real-lifeapplications. Imaging conditions and techniques vary considerably and each application has its own requirements for detection speed and accuracy. In our earlier works we used uniform partitioning with several approaches for detection of various types of 1D and 2D barcodes and showed their behaviour on a set of test images. In this work, we extend the partitioning idea and replace scan-line based methods with distance transformation to improve accuracy.
1 aBodnár, Péter1 aNyúl, László, Gábor1 aLinsen, L uhttp://www.actapress.com/PaperInfo.aspx?paperId=45498801094nas a2200157 4500008004100000245006000041210005800101260003900159300001400198520053100212100002000743700002800763700001900791700002300810856010300833 2013 eng d00aA Novel Method for Barcode Localization in Image Domain0 aNovel Method for Barcode Localization in Image Domain aBerlinbSpringer-VerlagcJune 2013 a189 - 1963 aBarcode localization is an essential step of the barcode readingprocess. 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.
1 aBodnár, Péter1 aNyúl, László, Gábor1 aKamel, Mohamed1 aCampilho, Aurélio uhttps://www.inf.u-szeged.hu/en/publication/a-novel-method-for-barcode-localization-in-image-domain00587nas a2200133 4500008004100000245009100041210007800132260003800210300001400248100002000262700002800282700002100310856012200331 2013 hun d00aVizuális kódok lokalizálásának javítása egyszerű jellemzők kombinációjával0 aVizuális kódok lokalizálásának javítása egyszerű jellemzők kombi aVeszprémbNJSZT-KÉPAFcJan 2013 a483 - 4951 aBodnár, Péter1 aNyúl, László, Gábor1 aCzúni, László uhttps://www.inf.u-szeged.hu/en/publication/vizualis-kodok-lokalizalasanak-javitasa-egyszeru-jellemzok-kombinaciojaval00866nas a2200109 4500008004100000245006700041210006700108260004900175300001200224520047500236856004500711 2012 eng d00aBarcode Detection with Morphological Operations and Clustering0 aBarcode Detection with Morphological Operations and Clustering aCrete, GreekbIASTED - Acta PresscJune 2012 a51 - 573 aBarcode detection has many applications and detection methods. Each application has its own requirements for speed and detection accuracy. Fine-tuning, upgrading or combining existing methods gives fast and robust solutions for detection. Modern computer vision techniques help the whole process to be fully automated. Different detection approaches are examined in this paper, and new methods are introduced.
uhttps://www.inf.u-szeged.hu/en/node/106100557nas a2200121 4500008004100000245007700041210006900118260007000187300001000257100002000267700002800287856012000315 2012 eng d00aBarcode Detection with Uniform Partitioning and Morphological Operations0 aBarcode Detection with Uniform Partitioning and Morphological Op aSzegedbUniversity of Szeged, Institute of InformaticscJune 2012 a4 - 51 aBodnár, Péter1 aNyúl, László, Gábor uhttps://www.inf.u-szeged.hu/en/publication/barcode-detection-with-uniform-partitioning-and-morphological-operations01575nas a2200181 4500008004100000245006900041210006900110260003400179300001400213520092800227100002001155700002801175700002101203700002001224700002001244700001701264856011201281 2012 eng d00aImproving barcode detection with combination of simple detectors0 aImproving barcode detection with combination of simple detectors aNaples, ItalybIEEEcNov 2012 a300 - 3063 aBarcode detection is required in a wide range of real-life applications. Imaging conditions and techniques vary considerably and each application has its own requirements for detection speed and accuracy. In our earlier works we built barcode detectors using morphological operations and uniform partitioning with several approaches and showed their behaviour on a set of test images. In this work, we examine ensemble efficiency of those simple detectors using various aggregation methods. Using a combination of several simple features localization performance improves significantly.
1 aBodnár, Péter1 aNyúl, László, Gábor1 aYetongnon, Kokou1 aChbeir, Richard1 aDipanda, Albert1 aGallo, Luigi uhttps://www.inf.u-szeged.hu/en/publication/improving-barcode-detection-with-combination-of-simple-detectors