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Author Oriol Pujol; Sergio Escalera; Petia Radeva edit  openurl
  Title An Incremental Node Embedding Technique for Error Correcting Output Codes Type Journal
  Year 2008 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 41 Issue 2 Pages 713–725  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PER2008 Serial 942  
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Author Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin edit  openurl
  Title Subclass Problem-Dependent Design for Error-Correcting Output Codes Type Journal
  Year 2008 Publication IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054 Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ ETP2008 Serial 951  
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Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  openurl
  Title Detection of Complex Salient Regions Type Journal
  Year 2008 Publication EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2008b Serial 960  
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Author Oriol Rodriguez-Leor; Carlo Gatta; E. Fernandez-Nofrerias; Oriol Pujol; Neus Salvatella; C. Bosch; H. Tizon; Petia Radeva; J. Mauri edit  openurl
  Title Computationally Efficient Image-based IVUS Pullbacks Gating Type Journal
  Year 2008 Publication European Heart Journal, ESC Supplement, Munich, 2008, p. 775 Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ RGF2008 Serial 1036  
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Author Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva edit  doi
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  Title Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification Type Journal Article
  Year 2009 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 10 Issue 1 Pages 113–126  
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  Abstract The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.  
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  ISSN 1524-9050 ISBN Medium  
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  Notes OR;MILAB;HuPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BEV2008 Serial 1116  
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