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Author Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva edit  doi
openurl 
  Title Circular Blurred Shape Model for Multiclass Symbol Recognition Type Journal Article
  Year 2011 Publication IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) Abbreviated Journal TSMCB  
  Volume 41 Issue 2 Pages 497-506  
  Keywords  
  Abstract In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication (up) Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1083-4419 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; DAG;HuPBA Approved no  
  Call Number Admin @ si @ EFP2011 Serial 1784  
Permanent link to this record
 

 
Author Sergio Escalera edit   pdf
url  openurl
  Title Multi-Modal Human Behaviour Analysis from Visual Data Sources Type Journal
  Year 2013 Publication ERCIM News journal Abbreviated Journal ERCIM  
  Volume 95 Issue Pages 21-22  
  Keywords  
  Abstract The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication (up) Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-4981 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ Esc2013 Serial 2361  
Permanent link to this record
 

 
Author Xavier Carrillo; E Fernandez-Nofrerias; Francesco Ciompi; O. Rodriguez-Leor; Petia Radeva; Neus Salvatella; Oriol Pujol; J. Mauri; A. Bayes edit  openurl
  Title Changes in Radial Artery Volume Assessed Using Intravascular Ultrasound: A Comparison of Two Vasodilator Regimens in Transradial Coronary Intervention Type Journal Article
  Year 2011 Publication Journal of Invasive Cardiology Abbreviated Journal JOIC  
  Volume 23 Issue 10 Pages 401-404  
  Keywords radial; vasodilator treatment; percutaneous coronary intervention; IVUS; volumetric IVUS analysis  
  Abstract OBJECTIVES:
This study used intravascular ultrasound (IVUS) to evaluate radial artery volume changes after intraarterial administration of nitroglycerin and/or verapamil.
BACKGROUND:
Radial artery spasm, which is associated with radial artery size, is the main limitation of the transradial approach in percutaneous coronary interventions (PCI).
METHODS:
This prospective, randomized study compared the effect of two intra-arterial vasodilator regimens on radial artery volume: 0.2 mg of nitroglycerin plus 2.5 mg of verapamil (Group 1; n = 15) versus 2.5 mg of verapamil alone (Group 2; n = 15). Radial artery lumen volume was assessed using IVUS at two time points: at baseline (5 minutes after sheath insertion) and post-vasodilator (1 minute after drug administration). The luminal volume of the radial artery was computed using ECOC Random Fields (ECOC-RF), a technique used for automatic segmentation of luminal borders in longitudinal cut images from IVUS sequences.
RESULTS:
There was a significant increase in arterial lumen volume in both groups, with an increase from 451 ± 177 mm³ to 508 ± 192 mm³ (p = 0.001) in Group 1 and from 456 ± 188 mm³ to 509 ± 170 mm³ (p = 0.001) in Group 2. There were no significant differences between the groups in terms of absolute volume increase (58 mm³ versus 53 mm³, respectively; p = 0.65) or in relative volume increase (14% versus 20%, respectively; p = 0.69).
CONCLUSIONS:
Administration of nitroglycerin plus verapamil or verapamil alone to the radial artery resulted in similar increases in arterial lumen volume according to ECOC-RF IVUS measurements.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication (up) 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 MILAB;HuPBA Approved no  
  Call Number Admin @ si @ CFC2011 Serial 1797  
Permanent link to this record
 

 
Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol edit  doi
openurl 
  Title Minimal Design of Error-Correcting Output Codes Type Journal Article
  Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 33 Issue 6 Pages 693-702  
  Keywords Multi-class classification; Error-correcting output codes; Ensemble of classifiers  
  Abstract IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
 
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication (up) Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0167-8655 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; OR;HuPBA;MV Approved no  
  Call Number Admin @ si @ BEB2011a Serial 1800  
Permanent link to this record
 

 
Author Carlo Gatta; Eloi Puertas; Oriol Pujol edit  doi
openurl 
  Title Multi-Scale Stacked Sequential Learning Type Journal Article
  Year 2011 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 44 Issue 10-11 Pages 2414-2416  
  Keywords Stacked sequential learning; Multiscale; Multiresolution; Contextual classification  
  Abstract One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication (up) 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  
  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ GPP2011 Serial 1802  
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