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Author |
Philippe Dosch; Josep Llados |
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Vectorial Signatures for Symbol Discrimination |
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Miscellaneous |
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2004 |
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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 |
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Springer-Verlag |
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DAG @ dag @ DoL2004 |
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461 |
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Author |
Jaume Gibert |
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Title |
Vector Space Embedding of Graphs via Statistics of Labelling Information |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Ernest Valveny |
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Admin @ si @ Gib2012 |
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2204 |
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Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books |
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2016 |
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15th international conference on Frontiers in Handwriting Recognition |
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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. |
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Shenzhen; China; October 2016 |
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ICFHR |
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DAG; 600.097; 602.006 |
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Admin @ si @ RFV2016 |
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2909 |
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Author |
Josep Llados;Horst Bunke; Enric Marti |
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Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes |
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1997 |
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Intelligent Robots: Sensing, Modeling and Planning |
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164-179 |
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Dagstuhl Workshop |
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World Scientific Press |
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9810231857 |
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DAG;IAM; |
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IAM @ iam @ LBM1997b |
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1563 |
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Josep Llados; Horst Bunke; Enric Marti |
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Using cyclic string matching to find rotational and reflectional symmetric shapes |
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1996 |
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Dagstuhl Seminar on Modelling and Planning for Sensor–based Intelligent Robot Systems |
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World Scientific |
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Saarbrucken (Germany). |
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R.C. Bolles, H.B.H.N. |
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DAG;IAM |
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IAM @ iam @ LBM1996 |
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1564 |
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Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero |
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Use case visual Bag-of-Words techniques for camera based identity document classification |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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721 - 725 |
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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. |
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Nancy; France; August 2015 |
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DAG; 600.077; 600.061; |
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Admin @ si @ HRL2015a |
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2726 |
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Fernando Vilariño |
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Unveiling the Social Impact of AI |
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2020 |
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Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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Admin @ si @ Vil2020 |
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3459 |
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Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Unsupervised writer style adaptation for handwritten word spotting |
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2008 |
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Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award. |
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Tampa, USA |
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DAG @ dag @ RPS2008 |
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1077 |
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Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Unsupervised writer adaptation of whole-word HMMs with application to word-spotting |
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2010 |
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Pattern Recognition Letters |
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PRL |
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742–749 |
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Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis |
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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. |
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Elsevier |
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DAG @ dag @ RPS2010 |
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1290 |
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Lluis Pere de las Heras; David Fernandez; Ernest Valveny; Josep Llados; Gemma Sanchez |
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Unsupervised wall detector in architectural floor plan |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1245-1249 |
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Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.061; 600.056; 600.045 |
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Admin @ si @ HFV2013 |
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2319 |
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