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Author Philippe Dosch; Josep Llados edit  openurl
  Title (down) Vectorial Signatures for Symbol Discrimination Type Miscellaneous
  Year 2003 Publication Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Barcelona  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ DoL2003 Serial 373  
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Author Philippe Dosch; Josep Llados edit  openurl
  Title (down) Vectorial Signatures for Symbol Discrimination Type Miscellaneous
  Year 2004 Publication Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:150–161, ISBN: 3–540–22478–5 Abbreviated Journal  
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  Abstract  
  Address Springer-Verlag  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ DoL2004 Serial 461  
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Author Jaume Gibert edit  openurl
  Title (down) Vector Space Embedding of Graphs via Statistics of Labelling Information Type Book Whole
  Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
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  Abstract Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.

Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding.

In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.
 
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Ernest Valveny  
  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ Gib2012 Serial 2204  
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Author Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez edit   pdf
openurl 
  Title (down) Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books Type Conference Article
  Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and
genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous
works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.
 
  Address Shenzhen; China; October 2016  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.097; 602.006 Approved no  
  Call Number Admin @ si @ RFV2016 Serial 2909  
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Author Josep Llados;Horst Bunke; Enric Marti edit  url
isbn  openurl
  Title (down) Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes Type Conference Article
  Year 1997 Publication Intelligent Robots: Sensing, Modeling and Planning Abbreviated Journal  
  Volume Issue Pages 164-179  
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  Abstract Dagstuhl Workshop  
  Address  
  Corporate Author Thesis  
  Publisher World Scientific Press Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 9810231857 Medium  
  Area Expedition Conference  
  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ LBM1997b Serial 1563  
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Author Josep Llados; Horst Bunke; Enric Marti edit  openurl
  Title (down) Using cyclic string matching to find rotational and reflectional symmetric shapes Type Conference Article
  Year 1996 Publication Dagstuhl Seminar on Modelling and Planning for Sensor–based Intelligent Robot Systems Abbreviated Journal  
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  Publisher World Scientific Place of Publication Saarbrucken (Germany). Editor R.C. Bolles, H.B.H.N.  
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  Notes DAG;IAM Approved no  
  Call Number IAM @ iam @ LBM1996 Serial 1564  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero edit  doi
openurl 
  Title (down) Use case visual Bag-of-Words techniques for camera based identity document classification Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 721 - 725  
  Keywords  
  Abstract Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes.  
  Address Nancy; France; August 2015  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.077; 600.061; Approved no  
  Call Number Admin @ si @ HRL2015a Serial 2726  
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Author Fernando Vilariño edit  openurl
  Title (down) Unveiling the Social Impact of AI Type Conference Article
  Year 2020 Publication Workshop at Digital Living Lab Days Conference Abbreviated Journal  
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  Address September 2020  
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  Notes MV; DAG; 600.121; 600.140;SIAI Approved no  
  Call Number Admin @ si @ Vil2020 Serial 3459  
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Author Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados edit  doi
openurl 
  Title (down) Unsupervised writer style adaptation for handwritten word spotting Type Conference Article
  Year 2008 Publication Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award. Abbreviated Journal  
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  Address Tampa, USA  
  Corporate Author Thesis  
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  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RPS2008 Serial 1077  
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Author Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados edit  url
doi  openurl
  Title (down) Unsupervised writer adaptation of whole-word HMMs with application to word-spotting Type Journal Article
  Year 2010 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 31 Issue 8 Pages 742–749  
  Keywords Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis  
  Abstract In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.

Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition.
 
  Address  
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  Publisher Elsevier Place of Publication Editor  
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  ISSN ISBN Medium  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ RPS2010 Serial 1290  
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