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.

%B Image Analysis and Recognition (ICIAR) %S Lecture Notes In Computer Science %I Springer-Verlag %C Vilamura, Portugal %8 Oct 2014 %G eng %9 Conference paper %0 Book Section %B Image Analysis and Recognition (ICIAR) %D 2013 %T A Novel Method for Barcode Localization in Image Domain %A Péter Bodnár %A László Gábor Nyúl %E Mohamed Kamel %E Aurélio Campilho %XBarcode 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.

%B Image Analysis and Recognition (ICIAR) %S Lecture Notes in Computer Science %I Springer-Verlag %C Berlin %P 189 - 196 %8 June 2013 %G eng %9 Conference paper %R 10.1007/978-3-642-39094-4_22 %0 Book Section %B Image Analysis and Recognition %D 2012 %T A central reconstruction based strategy for selecting projection angles in binary tomography %A Péter Balázs %A Joost K Batenburg %E Aurélio Campilho %E Mohamed Kamel %XIn 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.

%B Image Analysis and Recognition %S Lecture Notes in Computer Science %I Springer %C Berlin; Heidelberg; New York; London; Paris; Tokyo %P 382 - 391 %8 June 2012 %G eng %9 Conference paper %! LNCS %R 10.1007/978-3-642-31295-3_45 %0 Book Section %B International Conference on Image Analysis and Recognition (ICIAR) %D 2012 %T A Unifying Framework for Correspondence-less Linear Shape Alignment %A Zoltan Kato %E Aurélio Campilho %XWe 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.

%B International Conference on Image Analysis and Recognition (ICIAR) %S Lecture Notes in Computer Science %I Springer Verlag %C Aveiro, Portugal %P 277 - 284 %8 June 2012 %@ 978-3-642-31294-6 %G eng %9 Conference paper %! LNCS %R 10.1007/978-3-642-31295-3_33 %0 Book Section %B Proceedings of the International Conference on Image Analysis and Recognition (ICIAR) %D 2010 %T Topology Preserving 3D Thinning Algorithms using Four and Eight Subfields %A Gábor Németh %A Péter Kardos %A Kálmán Palágyi %E Aurélio Campilho %E Mohamed Kamel %X

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.

%B Proceedings of the International Conference on Image Analysis and Recognition (ICIAR) %S Lecture Notes in Computer Science %I Springer Verlag %C Póvoa de Varzim, Portugal %V 6111 %P 316 - 325 %8 June 2010 %G eng %9 Conference paper %! LNCS %R 10.1007/978-3-642-13772-3_32 %0 Book Section %B Image Analysis and Recognition %D 2008 %T Binary image registration using covariant gaussian densities %A Csaba Domokos %A Zoltan Kato %E Aurélio Campilho %XWe 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.

%B Image Analysis and Recognition %S Lecture Notes in Computer Science %I Springer %C Póvoa de Varzim, Portugal %P 455 - 464 %8 June 2008 %@ 978-3-540-69811-1 %G eng %9 Conference paper %! LNCS %R 10.1007/978-3-540-69812-8_45