Barcode 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.

1 aBodnár, Péter1 aNyúl, László, Gábor1 aPetrou, M1 aSappa, A D1 aTriantafyllidis, A G uhttps://www.inf.u-szeged.hu/publication/barcode-detection-with-morphological-operations-and-clustering01379nas a2200169 4500008004100000245005600041210005400097260004700151300001200198520082900210100001901039700002301058700001401081700001601095700002501111856007301136 2012 eng d00aIsthmus-based Order-Independent Sequential Thinning0 aIsthmusbased OrderIndependent Sequential Thinning aCrete, GreekbIASTED ACTA PresscJune 2012 a28 - 343 aThinning as a layer-by-layer reduction is a frequently used technique for skeletonization. Sequential thinning algorithms usually suffer from the drawback of being order-dependent, i.e., their results depend on the visiting order of object points. Earlier order-independent sequential methods are based on the conventional thinning schemes that preserve endpoints to provide relevant geometric information of objects. These algorithms can generate centerlines in 2D and medial surfaces in 3D. This paper presents an alternative strategy for order-independent thinning which follows an approach, proposed by Bertrand and Couprie, which accumulates so-called isthmus points. The main advantage of this order-independent strategy over the earlier ones is that it makes also possible to produce centerlines of 3D objects.

1 aKardos, Péter1 aPalágyi, Kálmán1 aPetrou, M1 aSappa, A, D1 aTriantafyllidis, A G uhttp://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=73601180nas a2200181 4500008004100000245008800041210006900129260004700198300001400245520049500259100002500754700001600779700002000795700001400815700001600829700002500845856012800870 2012 eng d00aPerimeter estimation of some discrete sets from horizontal and vertical projections0 aPerimeter estimation of some discrete sets from horizontal and v aCrete, GreekbIASTED ACTA PresscJune 2012 a174 - 1813 aIn this paper, we design neural networks to estimate the perimeter of simple and more complex discrete sets from their horizontal and vertical projections. The information extracted this way can be useful to simplify the problem of reconstructing the discrete set from its projections, which task is in focus of discrete tomography. Beside presenting experimental results with neural networks, we also reveal some statistical properties of the perimeter of the studied discrete sets.

1 aTasi, Tamás Sámuel1 aHegedűs, M1 aBalázs, Péter1 aPetrou, M1 aSappa, A, D1 aTriantafyllidis, A G uhttps://www.inf.u-szeged.hu/publication/perimeter-estimation-of-some-discrete-sets-from-horizontal-and-vertical-projections01745nas a2200181 4500008004100000020002300041245007900064210006900143260003700212300001400249520109300263100002001356700001401376700001601390700001701406700002101423856011901444 2007 eng d a978-953-184-116-0 00aReconstructing some hv-convex binary images from three or four projections0 aReconstructing some hvconvex binary images from three or four pr aIstanbul, TurkeybIEEEcSep 2007 a136 - 1403 aThe reconstruction of binary images from their projections is animportant problem in discrete tomography. The main challenge in this task is that in certain cases the projections do not uniquely determine the binary image. This can yield an extremely large number of (sometimes very different) solutions. Moreover, under certain circumstances the reconstruction becomes NP-hard. A commonly used technique to reduce ambiguity and to avoid intractability is to suppose that the image to be reconstructed arises from a certain class of images having some geometrical properties. This paper studies the reconstruction problem in the class of hv-convex images having their components in so-called decomposable configurations. First, we give a negative result showing that there can be exponentially many images of the above class having the same three projections. Then, we present a heuristic that uses four projections to reconstruct an hv-convex image with decomposable configuration. We also analyze the performance of our heuristic from the viewpoints of accuracy and running time.

1 aBalázs, Péter1 aPetrou, M1 aSaramaki, T1 aErcil, Aytul1 aLončarić, Sven uhttps://www.inf.u-szeged.hu/publication/reconstructing-some-hv-convex-binary-images-from-three-or-four-projections