01170nas a2200145 4500008004100000245006700041210006700108260005000175520059600225100002000821700002800841700001900869700002300888856011300911 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://home.inf.u-szeged.hu/en/publication/qr-code-localization-using-boosted-cascade-of-weak-classifiers-001095nas a2200157 4500008004100000245006000041210005800101260003900159300001400198520053100212100002000743700002800763700001900791700002300810856010400833 2013 eng d00aA Novel Method for Barcode Localization in Image Domain0 aNovel Method for Barcode Localization in Image Domain aBerlinbSpringer-VerlagcJune 2013 a189 - 1963 a

Barcode 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://home.inf.u-szeged.hu/en/publication/a-novel-method-for-barcode-localization-in-image-domain01192nas a2200157 4500008004100000245009700041210006900138260007600207300001400283520051000297100002000807700002400827700002300851700001900874856014100893 2012 eng d00aA central reconstruction based strategy for selecting projection angles in binary tomography0 acentral reconstruction based strategy for selecting projection a aBerlin; Heidelberg; New York; London; Paris; TokyobSpringercJune 2012 a382 - 3913 a

In this paper we propose a novel strategy for selecting projection angles in binary tomography which yields significantly more accurate reconstructions than others. In contrast with previous works which are of experimental nature, the method we present is based on theoretical observations. We report on experiments for different phantom images to show the effectiveness and roboustness of our procedure. The practically important case of noisy projections is also studied. © 2012 Springer-Verlag.

1 aBalázs, Péter1 aBatenburg, Joost, K1 aCampilho, Aurélio1 aKamel, Mohamed uhttps://home.inf.u-szeged.hu/en/publication/a-central-reconstruction-based-strategy-for-selecting-projection-angles-in-binary-tomography01363nas a2200145 4500008004100000020002200041245007200063210006900135260004900204300001400253520079400267100001701061700002301078856011601101 2012 eng d a978-3-642-31294-600aA Unifying Framework for Correspondence-less Linear Shape Alignment0 aUnifying Framework for Correspondenceless Linear Shape Alignment aAveiro, PortugalbSpringer VerlagcJune 2012 a277 - 2843 a

We consider the estimation of linear transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. Here we propose a unified framework where the exact transformation is obtained as the solution of either a polynomial or a linear system of equations without establishing correspondences. The advantages of the proposed solutions are that they are fast, easy to implement, have linear time complexity, work without landmark correspondences and are independent of the magnitude of transformation.

 

1 aKato, Zoltan1 aCampilho, Aurélio uhttps://home.inf.u-szeged.hu/en/publication/a-unifying-framework-for-correspondence-less-linear-shape-alignment01490nas a2200181 4500008004100000245007800041210006900119260005900188300001400247490000900261520081200270100002001082700001901102700002301121700002301144700001901167856012201186 2010 eng d00aTopology Preserving 3D Thinning Algorithms using Four and Eight Subfields0 aTopology Preserving 3D Thinning Algorithms using Four and Eight  aPóvoa de Varzim, PortugalbSpringer VerlagcJune 2010 a316 - 3250 v61113 a

Thinning is a frequently applied technique for extracting skeleton-like shape features (i.e., centerline, medial surface, and topological kernel) from volumetric binary images. Subfield-based thinning algorithms partition the image into some subsets which are alternatively activated, and some points in the active subfield are deleted. This paper presents a set of new 3D parallel subfield-based thinning algorithms that use four and eight subfields. The three major contributions of this paper are: 1) The deletion rules of the presented algorithms are derived from some sufficient conditions for topology preservation. 2) A novel thinning scheme is proposed that uses iteration-level endpoint checking. 3) Various characterizations of endpoints yield different algorithms. © 2010 Springer-Verlag.

1 aNémeth, Gábor1 aKardos, Péter1 aPalágyi, Kálmán1 aCampilho, Aurélio1 aKamel, Mohamed uhttps://home.inf.u-szeged.hu/en/publication/topology-preserving-3d-thinning-algorithms-using-four-and-eight-subfields01520nas a2200157 4500008004100000020002200041245006500063210006500128260005200193300001400245520093500259100001901194700001701213700002301230856010901253 2008 eng d a978-3-540-69811-100aBinary image registration using covariant gaussian densities0 aBinary image registration using covariant gaussian densities aPóvoa de Varzim, PortugalbSpringercJune 2008 a455 - 4643 a

We consider the estimation of 2D affine transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. In this paper, we propose a novel approach where the exact transformation is obtained as a least-squares solution of a linear system. The basic idea is to fit a Gaussian density to the shapes which preserves the effect of the unknown transformation. It can also be regarded as a consistent coloring of the shapes yielding two rich functions defined over the two shapes to be matched. The advantage of the proposed solution is that it is fast, easy to implement, works without established correspondences and provides a unique and exact solution regardless of the magnitude of transformation. © 2008 Springer-Verlag Berlin Heidelberg.

1 aDomokos, Csaba1 aKato, Zoltan1 aCampilho, Aurélio uhttps://home.inf.u-szeged.hu/en/publication/binary-image-registration-using-covariant-gaussian-densities