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Author Pau Riba; Alicia Fornes; Josep Llados edit   pdf
url  isbn
openurl 
  Title Towards the Alignment of Handwritten Music Scores Type Book Chapter
  Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages 103-116  
  Keywords Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment  
  Abstract It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Bart Lamiroy; R Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN (down) ISBN 978-3-319-52158-9 Medium  
  Area Expedition Conference  
  Notes DAG; 600.097; 602.006; 600.121 Approved no  
  Call Number Admin @ si @ RFL2017 Serial 2955  
Permanent link to this record
 

 
Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit  openurl
  Title Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary Type Book Chapter
  Year 2016 Publication Recent Trends in Image Processing and Pattern Recognition Abbreviated Journal  
  Volume 709 Issue Pages  
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  ISSN (down) ISBN Medium  
  Area Expedition Conference RTIP2R  
  Notes DAG Approved no  
  Call Number Admin @ si @ HTR2016 Serial 2956  
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Author Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell edit   pdf
url  openurl
  Title Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer Type Conference Article
  Year 2017 Publication 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging Abbreviated Journal  
  Volume 10550 Issue Pages 151-159  
  Keywords  
  Abstract Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.

The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.

We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
 
  Address Quebec; Canada; September 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN (down) ISBN Medium  
  Area Expedition Conference CLIP  
  Notes IAM; 600.096; 600.075; 600.145;DAG Approved no  
  Call Number Admin @ si @ GRM2017 Serial 2957  
Permanent link to this record
 

 
Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados edit  doi
openurl 
  Title Learning structural loss parameters on graph embedding applied on symbolic graphs Type Conference Article
  Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.  
  Address Kyoto; Japan; November 2017  
  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 (down) ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JRL2017b Serial 3073  
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Author Marçal Rusiñol; J. Chazalon; Katerine Diaz edit   pdf
doi  openurl
  Title Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness Type Journal Article
  Year 2018 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 77 Issue 11 Pages 13773-13798  
  Keywords Augmented reality; Document image matching; Educational applications  
  Abstract This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here.  
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  Notes DAG; ADAS; 600.084; 600.121; 600.118; 600.129 Approved no  
  Call Number Admin @ si @ RCD2018 Serial 2996  
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Author J. Chazalon; P. Gomez-Kramer; Jean-Christophe Burie; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; Nibal Nayef; Marçal Rusiñol; N. Sidere; Jean-Marc Ogier edit   pdf
doi  openurl
  Title SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode Type Conference Article
  Year 2017 Publication 1st International Workshop on Open Services and Tools for Document Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of
understanding, usage and improvement.
 
  Address Kyoto; Japan; November 2017  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (down) ISBN Medium  
  Area Expedition Conference ICDAR-OST  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ CGB2017 Serial 2997  
Permanent link to this record
 

 
Author Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset.
 
  Address Kyoto; Japan; November 2017  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GRK2017 Serial 2999  
Permanent link to this record
 

 
Author E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara edit   pdf
doi  openurl
  Title Benchmarking Keypoint Filtering Approaches for Document Image Matching Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy.
 
  Address Kyoto; Japan; November 2017  
  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 (down) ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ RCR2017 Serial 3000  
Permanent link to this record
 

 
Author David Aldavert; Marçal Rusiñol; Ricardo Toledo edit   pdf
doi  openurl
  Title Automatic Static/Variable Content Separation in Administrative Document Images Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
 
  Address Kyoto; Japan; November 2017  
  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 (down) ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121;ADAS Approved no  
  Call Number Admin @ si @ ART2017 Serial 3001  
Permanent link to this record
 

 
Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Learning to Learn from Web Data through Deep Semantic Embeddings Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 11134 Issue Pages 514-529  
  Keywords  
  Abstract In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.  
  Address Munich; Alemanya; September 2018  
  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 (down) ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 600.129; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ GGG2018a Serial 3175  
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