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Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  openurl
  Title Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes Type Journal
  Year 2007 Publication Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2007c Serial 907  
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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 Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera edit  doi
openurl 
  Title Automatic Detection of Dominance and Expected Interest Type Journal Article
  Year 2010 Publication EURASIP Journal on Advances in Signal Processing Abbreviated Journal EURASIPJ  
  Volume (up) Issue Pages 12  
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  Abstract Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
 
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  ISSN 1110-8657 ISBN Medium  
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  Notes OR;MILAB;HUPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2010d Serial 1283  
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