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Author S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes edit  url
openurl 
  Title (up) Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement Type Journal Article
  Year 2022 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 123 Issue Pages 108370  
  Keywords  
  Abstract Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task.  
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  Notes DAG; 600.124; 600.121; 602.230 Approved no  
  Call Number Admin @ si @ JSK2022 Serial 3613  
Permanent link to this record
 
 
Author Ricardo Toledo; Ramon Baldrich; Ernest Valveny; Petia Radeva edit  openurl
  Title (up) Enhancing snakes for vessel detection in angiography images. Type Miscellaneous
  Year 2002 Publication Proceedings of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 139–144. Abbreviated Journal  
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  Notes MILAB;DAG;CIC;ADAS Approved no  
  Call Number BCNPCL @ bcnpcl @ TBV2002 Serial 300  
Permanent link to this record
 
 
Author Pau Riba; Josep Llados; Alicia Fornes edit   pdf
doi  openurl
  Title (up) Error-tolerant coarse-to-fine matching model for hierarchical graphs Type Conference Article
  Year 2017 Publication 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition Abbreviated Journal  
  Volume 10310 Issue Pages 107-117  
  Keywords Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching  
  Abstract Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.  
  Address Anacapri; Italy; May 2017  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor Pasquale Foggia; Cheng-Lin Liu; Mario Vento  
  Language Summary Language Original Title  
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  ISSN ISBN Medium  
  Area Expedition Conference GbRPR  
  Notes DAG; 600.097; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ RLF2017a Serial 2951  
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Author Youssef El Rhabi; Simon Loic; Brun Luc edit   pdf
url  openurl
  Title (up) Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel Type Conference Article
  Year 2015 Publication 15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 Abbreviated Journal  
  Volume Issue Pages  
  Keywords Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration  
  Abstract Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data.  
  Address Amiens; France; June 2015  
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  Area Expedition Conference ORASIS  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ RLL2015 Serial 2626  
Permanent link to this record
 
 
Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil edit   pdf
openurl 
  Title (up) Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías Type Miscellaneous
  Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal  
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  Address Tarragona; juliol 2014  
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  Area Expedition Conference CIDUI  
  Notes IAM; 600.075;DAG Approved no  
  Call Number Admin @ si @ SRM2014 Serial 2458  
Permanent link to this record
 
 
Author Salim Jouili; Salvatore Tabbone; Ernest Valveny edit  isbn
openurl 
  Title (up) Evaluation of graph matching measures for documents retrieval Type Conference Article
  Year 2009 Publication In proceedings of 8th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 13–21  
  Keywords Graph Matching; Graph retrieval; structural representation; Performance Evaluation  
  Abstract In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used which include line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each grahp distance measure depends on the kind of data and the graph representation technique.  
  Address La Rochelle, France  
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  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-13727-3 Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number DAG @ dag @ JTV2009a Serial 1230  
Permanent link to this record
 
 
Author Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero edit  doi
openurl 
  Title (up) Evaluation of Texture Descriptors for Validation of Counterfeit Documents Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1237-1242  
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  Abstract This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance.  
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  ISSN 2379-2140 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.061; 601.269; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ BRL2017 Serial 3092  
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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title (up) Evaluation of the Effect of Improper Segmentation on Word Spotting Type Journal Article
  Year 2019 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 22 Issue Pages 361-374  
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  Abstract Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.  
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  Notes DAG; 600.097; 600.084; 600.121; 600.140; 600.129 Approved no  
  Call Number Admin @ si @ DNL2019 Serial 3455  
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Author Miquel Ferrer; Ernest Valveny; F. Serratosa; Horst Bunke edit  openurl
  Title (up) Exact Median Graph Computation via Graph Embedding Type Conference Article
  Year 2008 Publication 12th International Workshop on Structural and Syntactic Pattern Recognition Abbreviated Journal  
  Volume 5324 Issue Pages 15–24  
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  Address Orlando – Florida (USA)  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference SSPR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ FVS2008b Serial 1076  
Permanent link to this record
 
 
Author Lluis Gomez edit  openurl
  Title (up) Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding Type Book Whole
  Year 2016 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
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  Abstract This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme.		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Place of Publication Editor Dimosthenis Karatzas  
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  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ Gom2016 Serial 2891  
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