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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
doi  openurl
  Title Local Binary Pattern for Word Spotting in Handwritten Historical Document Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume Issue Pages 574-583  
  Keywords Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data  
  Abstract Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm.  
  Address Merida; Mexico; December 2016  
  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 ISBN Medium  
  Area Expedition Conference S+SSPR  
  Notes DAG; 600.097; 602.006; 603.053 Approved no  
  Call Number Admin @ si @ DNL2016 Serial 2876  
Permanent link to this record
 
 
Author Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras edit  doi
openurl 
  Title Information Theoretic Rotationwise Robust Binary Descriptor Learning Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume Issue Pages 368-378  
  Keywords  
  Abstract In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.  
  Address Mérida; Mexico; November 2016  
  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 S+SSPR  
  Notes DAG; ADAS; 600.097; 600.086 Approved no  
  Call Number Admin @ si @ RLL2016 Serial 2871  
Permanent link to this record
 
 
Author Suman Ghosh; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title A Sliding Window Framework for Word Spotting Based on Word Attributes Type Conference Article
  Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal  
  Volume 9117 Issue Pages 652-661  
  Keywords Word spotting; Sliding window; Word attributes  
  Abstract In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets.  
  Address Santiago de Compostela; June 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-19389-2 Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ GhV2015b Serial 2716  
Permanent link to this record
 
 
Author Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño edit   pdf
doi  openurl
  Title Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos Type Conference Article
  Year 2014 Publication CARE workshop Abbreviated Journal  
  Volume Issue Pages  
  Keywords Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection  
  Abstract We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.  
  Address Boston; USA; September 2014  
  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 CARE  
  Notes MV; DAG; 600.060; 600.047; 600.077;SIAI Approved no  
  Call Number Admin @ si @ NBF2014 Serial 2504  
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Author Francesco Brughi; Debora Gil; Llorenç Badiella; Eva Jove Casabella; Oriol Ramos Terrades edit   pdf
doi  isbn
openurl 
  Title Exploring the impact of inter-query variability on the performance of retrieval systems Type Conference Article
  Year 2014 Publication 11th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 8814 Issue Pages 413–420  
  Keywords  
  Abstract This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes.  
  Address Algarve; Portugal; October 2014  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-11757-7 Medium  
  Area Expedition Conference ICIAR  
  Notes IAM; DAG; 600.060; 600.061; 600.077; 600.075 Approved no  
  Call Number Admin @ si @ BGB2014 Serial 2559  
Permanent link to this record
 
 
Author Arnau Baro; Pau Riba; Alicia Fornes edit  doi
openurl 
  Title Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network Type Conference Article
  Year 2022 Publication Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) Abbreviated Journal  
  Volume 13639 Issue Pages 171-184  
  Keywords Object detection; Optical music recognition; Graph neural network  
  Abstract During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results.  
  Address December 04 – 07, 2022; Hyderabad, India  
  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 ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.162; 600.140; 602.230 Approved no  
  Call Number Admin @ si @ BRF2022b Serial 3740  
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Author Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli edit   pdf
doi  openurl
  Title A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts Type Conference Article
  Year 2022 Publication Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) Abbreviated Journal  
  Volume 13639 Issue Pages 3-12  
  Keywords N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections  
  Abstract Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction.  
  Address December 04 – 07, 2022; Hyderabad, India  
  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 ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ GBS2022 Serial 3733  
Permanent link to this record
 
 
Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) edit  doi
isbn  openurl
  Title Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 Type Book Whole
  Year 2022 Publication Frontiers in Handwriting Recognition. Abbreviated Journal  
  Volume 13639 Issue Pages  
  Keywords  
  Abstract  
  Address ICFHR 2022, Hyderabad, India, December 4–7, 2022  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor Utkarsh Porwal; Alicia Fornes; Faisal Shafait  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-21648-0 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ PFS2022 Serial 3809  
Permanent link to this record
 
 
Author Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados edit   pdf
doi  openurl
  Title Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis Type Conference Article
  Year 2022 Publication Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 Abbreviated Journal  
  Volume 13424 Issue Pages 336-348  
  Keywords Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk  
  Abstract Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.  
  Address June 7-9, 2022, Las Palmas de Gran Canaria, Spain  
  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 ISBN Medium  
  Area Expedition Conference IGS  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ BFC2022 Serial 3738  
Permanent link to this record
 
 
Author Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title A Multilingual Approach to Scene Text Visual Question Answering Type Conference Article
  Year 2022 Publication Document Analysis Systems.15th IAPR International Workshop, (DAS2022) Abbreviated Journal  
  Volume Issue Pages 65-79  
  Keywords Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning  
  Abstract Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines.  
  Address La Rochelle, France; May 22–25, 2022  
  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 DAS  
  Notes DAG; 611.004; 600.155; 601.002 Approved no  
  Call Number Admin @ si @ BGK2022b Serial 3695  
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