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Author S. Chanda; Umapada Pal; Oriol Ramos Terrades
Title Word-Wise Thai and Roman Script Identification Type Journal
Year 2009 Publication ACM Transactions on Asian Language Information Processing Abbreviated Journal TALIP
Volume 8 Issue 3 Pages 1-21
Keywords
Abstract In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1530-0226 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ CPR2009f Serial 1869
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Author D. Perez; L. Tarazon; N. Serrano; F.M. Castro; Oriol Ramos Terrades; A. Juan
Title The GERMANA Database Type Conference Article
Year 2009 Publication 10th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 301-305
Keywords
Abstract A new handwritten text database, GERMANA, is presented to facilitate empirical comparison of different approaches to text line extraction and off-line handwriting recognition. GERMANA is the result of digitising and annotating a 764-page Spanish manuscript from 1891, in which most pages only contain nearly calligraphed text written on ruled sheets of well-separated lines. To our knowledge, it is the first publicly available database for handwriting research, mostly written in Spanish and comparable in size to standard databases. Due to its sequential book structure, it is also well-suited for realistic assessment of interactive handwriting recognition systems. To provide baseline results for reference in future studies, empirical results are also reported, using standard techniques and tools for preprocessing, feature extraction, HMM-based image modelling, and language modelling.
Address Barcelona; Spain
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1520-5363 ISBN 978-1-4244-4500-4 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ PTS2009 Serial 1870
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Author L.Tarazon; D. Perez; N. Serrano; V. Alabau; Oriol Ramos Terrades; A. Sanchis; A. Juan
Title Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text Type Conference Article
Year 2009 Publication 15th International Conference on Image Analysis and Processing Abbreviated Journal
Volume 5716 Issue Pages 567-574
Keywords
Abstract An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.
Address Vietri sul Mare, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-04145-7 Medium
Area Expedition Conference ICIAP
Notes DAG Approved no
Call Number Admin @ si @ TPS2009 Serial 1871
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Author H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent
Title Feature Selection Combining Genetic Algorithm and Adaboost Classifiers Type Conference Article
Year 2008 Publication 19th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 1-4
Keywords
Abstract
Address Tampa, Florida
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes DAG Approved no
Call Number Admin @ si @ CRT2008 Serial 1872
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Author T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades
Title Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval Type Conference Article
Year 2008 Publication Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, Abbreviated Journal
Volume Issue Pages 191-197
Keywords
Abstract
Address Nara, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DAS
Notes DAG Approved no
Call Number Admin @ si @ NTR2008a Serial 1873
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Author H. Chouaib; Salvatore Tabbone; Oriol Ramos Terrades; F. Cloppet; N. Vincent; A.T. Thierry Paquet
Title Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost Type Conference Article
Year 2008 Publication Colloque International Francophone sur l'Ecrit et le Document Abbreviated Journal
Volume Issue Pages 181-186
Keywords
Abstract
Address Rouen, France
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CIFED
Notes DAG Approved no
Call Number Admin @ si @ CTR2008 Serial 1874
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Author T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades; A.T. Thierry
Title Proposition d'un descripteur de formes et du modèle vectoriel pour la recherche de symboles Type Conference Article
Year 2008 Publication Colloque International Francophone sur l'Ecrit et le Document Abbreviated Journal
Volume Issue Pages 79-84
Keywords
Abstract
Address Rouen, France
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CIFED
Notes DAG Approved no
Call Number Admin @ si @ NTR2008b Serial 1875
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Author Salvatore Tabbone; Oriol Ramos Terrades; S. Barrat
Title Histogram of radon transform. A useful descriptor for shape retrieval Type Conference Article
Year 2008 Publication 19th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 1-4
Keywords
Abstract
Address Tampa, Florida
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes DAG Approved no
Call Number Admin @ si @ TRB2008 Serial 1876
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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa
Title Multiple target tracking for intelligent headlights control Type Journal Article
Year 2012 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 13 Issue 2 Pages 594-605
Keywords Intelligent Headlights
Abstract Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g Serial 1877
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Author V.C.Kieu; Alicia Fornes; M. Visani; N.Journet ; Anjan Dutta
Title The ICDAR/GREC 2013 Music Scores Competition on Staff Removal Type Conference Article
Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords Competition; Music scores; Staff Removal
Abstract The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we propose a staff removal competition where we simulate old music scores. Thus, we have created a new set of images, which contain noise and 3D distortions. This paper describes the distortion methods, metrics, the participant’s methods and the obtained results.
Address Bethlehem; PA; USA; August 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG; 600.045; 600.061 Approved no
Call Number Admin @ si @ KFV2013 Serial 2337
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Author M. Visani; V.C.Kieu; Alicia Fornes; N.Journet
Title The ICDAR 2013 Music Scores Competition: Staff Removal Type Conference Article
Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1439-1443
Keywords
Abstract The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.
Address Washington; USA; August 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1520-5363 ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.045; 600.061 Approved no
Call Number Admin @ si @ VKF2013 Serial 2338
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Author Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo
Title Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Computer Graphics Forum Abbreviated Journal CGF
Volume 30 Issue 7 Pages 2107-2115
Keywords
Abstract IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; HuPBA Approved no
Call Number Admin @ si @ EPA2011 Serial 1881
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Author Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Xavier Jimenez ; Oscar Vilarroya; Petia Radeva
Title A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder Type Journal Article
Year 2011 Publication BioMedical Engineering Online Abbreviated Journal BEO
Volume 10 Issue 105 Pages 1-23
Keywords Brain caudate nucleus; segmentation; MRI; atlas-based strategy; Graph Cut framework
Abstract Background
Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations.

Method
We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure.

Results
We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis.

Conclusion
CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1475-925X ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ ISH2011 Serial 1882
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Author Mario Rojas; David Masip; A. Todorov; Jordi Vitria
Title Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models Type Journal Article
Year 2011 Publication PloS one Abbreviated Journal Plos
Volume 6 Issue 8 Pages e23323
Keywords
Abstract JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions
Address
Corporate Author Thesis
Publisher Public Library of Science Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number Admin @ si @ RMT2011 Serial 1883
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Author Bogdan Raducanu; Fadi Dornaika
Title A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR
Volume 45 Issue 6 Pages 2432-2444
Keywords
Abstract IF= 2.61
IF=2.61 (2010)
In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other.

Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language (down) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes OR; MV Approved no
Call Number Admin @ si @ RaD2012a Serial 1884
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