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Author R. Clariso; David Masip; A. Rius edit  url
openurl 
  Title Student projects empowering mobile learning in higher education Type Journal
  Year (down) 2014 Publication Revista de Universidad y Sociedad del Conocimiento Abbreviated Journal RUSC  
  Volume 11 Issue Pages 192-207  
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  ISSN 1698-580X ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ CMR2014 Serial 2619  
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Author Santiago Segui; Michal Drozdzal; Ekaterina Zaytseva; Fernando Azpiroz; Petia Radeva; Jordi Vitria edit   pdf
doi  openurl
  Title Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images Type Journal Article
  Year (down) 2014 Publication IEEE Transactions on Information Technology in Biomedicine Abbreviated Journal TITB  
  Volume 18 Issue 6 Pages 1831-1838  
  Keywords Wireless Capsule Endoscopy; Small Bowel Motility Dysfunction; Contraction Detection; Structured Prediction; Betweenness Centrality  
  Abstract Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the folding of the intestinal wall. In this paper we present a new method to robustly detect wrinkle frames in full WCE videos by using a new mid-level image descriptor that is based on a centrality measure proposed for graphs. We present an extended validation, carried out in a very large database, that shows that the proposed method achieves state of the art performance for this task.  
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  Notes OR; MILAB; 600.046;MV Approved no  
  Call Number Admin @ si @ SDZ2014 Serial 2385  
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Author Bogdan Raducanu; Fadi Dornaika edit   pdf
doi  openurl
  Title Texture-independent recognition of facial expressions in image snapshots and videos Type Journal Article
  Year (down) 2013 Publication Machine Vision and Applications Abbreviated Journal MVA  
  Volume 24 Issue 4 Pages 811-820  
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  Abstract This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines.  
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  Publisher Springer-Verlag Place of Publication Editor  
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  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes OR; 600.046; 605.203;MV Approved no  
  Call Number Admin @ si @ RaD2013 Serial 2230  
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Author Fadi Dornaika; Abdelmalik Moujahid; Bogdan Raducanu edit   pdf
url  doi
openurl 
  Title Facial expression recognition using tracked facial actions: Classifier performance analysis Type Journal Article
  Year (down) 2013 Publication Engineering Applications of Artificial Intelligence Abbreviated Journal EAAI  
  Volume 26 Issue 1 Pages 467-477  
  Keywords Visual face tracking; 3D deformable models; Facial actions; Dynamic facial expression recognition; Human–computer interaction  
  Abstract In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%.  
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  Publisher Elsevier Place of Publication Editor  
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  Notes OR; 600.046;MV Approved no  
  Call Number Admin @ si @ DMR2013 Serial 2185  
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu edit   pdf
openurl 
  Title Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes Type Journal
  Year (down) 2013 Publication Komputer Sapiens Abbreviated Journal KS  
  Volume 1 Issue Pages 20-25  
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  Notes OR;MV Approved no  
  Call Number Admin @ si @ TSR2013 Serial 2231  
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