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.

JF - Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition, and Applications (SPPRA) PB - IASTED - Acta Press CY - Crete, Greek N1 - ScopusID: 84864778306doi: 10.2316/P.2012.778-014 ER - TY - CONF T1 - Isthmus-based Order-Independent Sequential Thinning T2 - IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SSPRA) Y1 - 2012 A1 - Péter Kardos A1 - Kálmán Palágyi ED - M Petrou ED - A D Sappa ED - A G Triantafyllidis AB -Thinning 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.

JF - IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SSPRA) PB - IASTED ACTA Press CY - Crete, Greek UR - http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=736 N1 - doi: 10.2316/P.2012.778-025 ER - TY - CONF T1 - Perimeter estimation of some discrete sets from horizontal and vertical projections T2 - IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA) Y1 - 2012 A1 - Tamás Sámuel Tasi A1 - M Hegedűs A1 - Péter Balázs ED - M Petrou ED - A D Sappa ED - A G Triantafyllidis AB -In 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.

JF - IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA) PB - IASTED ACTA Press CY - Crete, Greek N1 - ScopusID: 84864772360doi: 10.2316/P.2012.778-017 ER - TY - CHAP T1 - Reconstructing some hv-convex binary images from three or four projections T2 - Proccedings of the 5th International Symposium on Image and Signal Processing and Analysis Y1 - 2007 A1 - Péter Balázs ED - M Petrou ED - T Saramaki ED - Aytul Ercil ED - Sven Lončarić AB -The 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.

JF - Proccedings of the 5th International Symposium on Image and Signal Processing and Analysis PB - IEEE CY - Istanbul, Turkey SN - 978-953-184-116-0 N1 - UT: 000253387900025ScopusID: 7949129892doi: 10.1109/ISPA.2007.4383678 ER -