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Author Oriol Rodriguez-Leon; Josefina Mauri;Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva edit  openurl
  Title Utilizacion de la estructura de los campos vectoriales para la deteccion de la Adventicia en imagenes de Ecografia Intracoronaria Type Journal
  Year 2004 Publication Revista Española de Cardiología Abbreviated Journal REC  
  Volume 57 Issue 2 Pages 100  
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  Notes MILAB;IAM Approved no  
  Call Number BCNPCL @ bcnpcl @ RMF2004 Serial 566  
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Author Oriol Rodriguez-Leon.A.Carol;H.Tizon; Eduard Fernandez-Nofrerias; Josefina Mauri; Vicente del Valle; Debora Gil; Aura Hernandez-Sabate; Petia Radeva edit  openurl
  Title Model estadístic-determinístic per la segmentació de l adventicia en imatges d ecografía intracoronaria Type Journal Article
  Year 2005 Publication Rev Societat Catalana Cardiologia Abbreviated Journal  
  Volume 5 Issue Pages 41  
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  Notes IAM;MILAB Approved no  
  Call Number IAM @ iam @ RCT2005 Serial 1637  
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Author Oriol Rodriguez-Leon; Josefina Mauri;Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva edit  openurl
  Title Utilización de la Estructura de los Campos Vectoriales para la Detección de la Adventicia en Imágenes de Ecografía Intracoronaria Type Journal Article
  Year 2004 Publication Revista Internacional de Enfermedades Cardiovasculares Revista Española de Cardiología Abbreviated Journal  
  Volume 57 Issue 2 Pages 100  
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  Notes IAM;MILAB Approved no  
  Call Number IAM @ iam @ RMF2004 Serial 1642  
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Author Enric Marti; J.Roncaries; Debora Gil; Aura Hernandez-Sabate; Antoni Gurgui; Ferran Poveda edit  doi
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  Title PBL On Line: A proposal for the organization, part-time monitoring and assessment of PBL group activities Type Journal
  Year 2015 Publication Journal of Technology and Science Education Abbreviated Journal JOTSE  
  Volume 5 Issue 2 Pages 87-96  
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  Notes IAM; ADAS; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MRG2015 Serial 2608  
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Author Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin edit   pdf
url  openurl
  Title Mathematics learning opportunities when playing a Tower Defense Game Type Journal
  Year 2015 Publication International Journal of Serious Games Abbreviated Journal IJSG  
  Volume 2 Issue 4 Pages 57-71  
  Keywords Tower Defense game; learning opportunities; mathematics; problem solving; game design  
  Abstract (up) A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.  
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  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ HJG2015 Serial 2730  
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Author Jaume Garcia; Debora Gil; Luis Badiella; Aura Hernandez-Sabate; Francesc Carreras; Sandra Pujades; Enric Marti edit   pdf
doi  openurl
  Title A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function Type Journal Article
  Year 2010 Publication IEEE Transactions on Medical Imaging Abbreviated Journal TMI  
  Volume 29 Issue 3 Pages 733-745  
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  Abstract (up) A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains.  
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  ISSN 0278-0062 ISBN Medium  
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  Notes IAM Approved no  
  Call Number IAM @ iam @ GGH2010b Serial 1507  
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Author Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez edit   pdf
doi  openurl
  Title A reduced feature set for driver head pose estimation Type Journal Article
  Year 2016 Publication Applied Soft Computing Abbreviated Journal ASOC  
  Volume 45 Issue Pages 98-107  
  Keywords Head pose estimation; driving performance evaluation; subspace based methods; linear regression  
  Abstract (up) Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.  
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  Notes ADAS; 600.085; 600.076; Approved no  
  Call Number Admin @ si @ DHL2016 Serial 2760  
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Author Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez edit  doi
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  Title Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem Type Journal
  Year 2020 Publication Mathematics Abbreviated Journal MATH  
  Volume 20 Issue 8(9) Pages 1595  
  Keywords STEM education; Project-based learning; Coding; software tool  
  Abstract (up) In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view.		  
  Address September 2020  
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  Notes IAM; ISE Approved no  
  Call Number Admin @ si @ Serial 3722  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil edit   pdf
doi  openurl
  Title Continuous head pose estimation using manifold subspace embedding and multivariate regression Type Journal Article
  Year 2018 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 6 Issue Pages 18325 - 18334  
  Keywords Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression  
  Abstract (up) In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees.  
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  ISSN 2169-3536 ISBN Medium  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DMH2018b Serial 3091  
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Author Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate edit   pdf
doi  openurl
  Title Feature Extraction by Using Dual-Generalized Discriminative Common Vectors Type Journal Article
  Year 2019 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 61 Issue 3 Pages 331-351  
  Keywords Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning  
  Abstract (up) In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.  
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  Notes DAG; ADAS; 600.084; 600.118; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DRR2019 Serial 3172  
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