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.

JF - Image Analysis and Recognition (ICIAR) T3 - Lecture Notes In Computer Science PB - Springer-Verlag CY - Vilamura, Portugal N1 - Art. No.: 225Accepted for publication ER - TY - CHAP T1 - A Novel Method for Barcode Localization in Image Domain T2 - Image Analysis and Recognition (ICIAR) Y1 - 2013 A1 - Péter Bodnár A1 - László Gábor Nyúl ED - Mohamed Kamel ED - Aurélio Campilho AB -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.

JF - Image Analysis and Recognition (ICIAR) T3 - Lecture Notes in Computer Science PB - Springer-Verlag CY - Berlin N1 - doi: 10.1007/978-3-642-39094-4_22 ER - TY - CHAP T1 - A central reconstruction based strategy for selecting projection angles in binary tomography T2 - Image Analysis and Recognition Y1 - 2012 A1 - Péter Balázs A1 - Joost K Batenburg ED - Aurélio Campilho ED - Mohamed Kamel AB -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.

JF - Image Analysis and Recognition T3 - Lecture Notes in Computer Science PB - Springer CY - Berlin; Heidelberg; New York; London; Paris; Tokyo N1 - ScopusID: 84864128031doi: 10.1007/978-3-642-31295-3_45 JO - LNCS ER - TY - CHAP T1 - A Unifying Framework for Correspondence-less Linear Shape Alignment T2 - International Conference on Image Analysis and Recognition (ICIAR) Y1 - 2012 A1 - Zoltan Kato ED - Aurélio Campilho AB -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.

JF - International Conference on Image Analysis and Recognition (ICIAR) T3 - Lecture Notes in Computer Science PB - Springer Verlag CY - Aveiro, Portugal SN - 978-3-642-31294-6 N1 - UT: 000323558000033 JO - LNCS ER - TY - CHAP T1 - Topology Preserving 3D Thinning Algorithms using Four and Eight Subfields T2 - Proceedings of the International Conference on Image Analysis and Recognition (ICIAR) Y1 - 2010 A1 - Gábor Németh A1 - Péter Kardos A1 - Kálmán Palágyi ED - Aurélio Campilho ED - Mohamed Kamel AB -

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.

JF - Proceedings of the International Conference on Image Analysis and Recognition (ICIAR) T3 - Lecture Notes in Computer Science PB - Springer Verlag CY - Póvoa de Varzim, Portugal VL - 6111 N1 - ScopusID: 77955432947doi: 10.1007/978-3-642-13772-3_32 JO - LNCS ER - TY - CHAP T1 - Binary image registration using covariant gaussian densities T2 - Image Analysis and Recognition Y1 - 2008 A1 - Csaba Domokos A1 - Zoltan Kato ED - Aurélio Campilho AB -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.

JF - Image Analysis and Recognition T3 - Lecture Notes in Computer Science PB - Springer CY - Póvoa de Varzim, Portugal SN - 978-3-540-69811-1 N1 - UT: 000257302500045ScopusID: 47749098390doi: 10.1007/978-3-540-69812-8_45 JO - LNCS ER -