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Author Enric Marti; Ferran Poveda; Antoni Gurgui; Debora Gil edit   pdf
url  isbn
openurl 
  Title Aprendizaje Basado en Proyectos en Ingeniería Informática. Resultados y reflexiones de seis años de experiencia Type Miscellaneous
  Year 2011 Publication (up) Actas del Simposio-Taller JENUI 2011 Abbreviated Journal  
  Volume Issue Pages 1-8  
  Keywords  
  Abstract In this workshop a 6 years experience in Project Based Learning (PBL) in Computer Graphics, Computer Engineering course at the Autonomous University of Barcelona (UAB) is presented. We use a Moodle environment suited to manage the documentation generated in PBL. The course is organized by means of two alternative routes: a classic itinerary of lectures and test-based evaluation and another with PBL. In the PBL itinerary we explain the organization in teamgroups, homework tutoring and monitoring and evaluation guidelines for students. We provide some of the work done by students, and the results of assessment surveys carried out to students during these years. We report the evolution of our PBL itinerary in terms of, both, organization and student surveys.
The workshop aims at discussing about on the advantages and disadvantages of using these active methodologies in technical degrees such as computer engineering, in order to debate about the most suitable way of organizing PBL and assessing students learning rate.
 
  Address Sevilla, Spain  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language spanish Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-694-5440-4 Medium  
  Area Expedition Conference JENUI  
  Notes IAM Approved no  
  Call Number IAM @ iam @ MPG2011 Serial 1584  
Permanent link to this record
 

 
Author Enric Marti; Debora Gil; Marc Vivet ; Carme Julia edit   pdf
openurl 
  Title Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática Type Miscellaneous
  Year 2008 Publication (up) Actas V Jornadas Internacionales de Innovación Universitaria Abbreviated Journal  
  Volume Issue Pages  
  Keywords Aprendizaje cooperativo; aprendizaje basado en proyectos; experiencias docentes.  
  Abstract En este trabajo se presentan los resultados de la aplicación de la metodología del aprendizaje cooperativo a la docencia de dos asignaturas de programación en ingeniería informática. ‘Algoritmos y programación’ y ‘Lenguajes de programación’ son dos asignaturas complementarias que se organizan entorno a un proyecto común que engloba los contenidos de ambas asignaturas. En la docencia de una parte muy importante de estas asignaturas, la metodología del aprendizaje cooperativo se ha adaptado a sus características específicas. Como muestra de esta adaptación presentamos dos ejemplos de las actividades desarrolladas dentro de la docencia de estas asignaturas. Después de tres años de aplicación, el análisis a nivel cualitativo y cuantitativo de los resultados muestra que éstos son muy satisfactorios y que la aplicación del método cooperativo ha mejorado de forma considerable el rendimiento de los alumnos en ambas asignaturas.  
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  Notes IAM;ADAS Approved no  
  Call Number IAM @ iam @ MGV2008a Serial 1598  
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Author David Guillamet; Jordi Vitria edit  openurl
  Title Local Discriminant Regions Using Support Vector Machines for Object Recognition. Type Miscellaneous
  Year 2000 Publication (up) Advances in Pattern Recognition, Lecture Notes in Computer Science 1876: 550–559, Springer Verlag. Abbreviated Journal  
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  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ GuV2000 a Serial 240  
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Author Enric Marti; Petia Radeva; Ricardo Toledo; Jordi Vitria edit  openurl
  Title Experiencia de aplicación de la metodología de aprendizaje por proyectos en asignaturas de Ingeniería Informática para una mejor adaptación a los créditos ECTS i al Espacio Europeo de Educación Superior Type Miscellaneous
  Year 2005 Publication (up) Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR) Abbreviated Journal  
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  Area Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR) Expedition Conference  
  Notes IAM;RV;OR;MILAB;ADAS;MV Approved no  
  Call Number IAM @ iam @ MRT2005 Serial 1608  
Permanent link to this record
 

 
Author Fernando Vilariño; Enric Marti edit  openurl
  Title New didactic techniques in the EHES applying mobile technologies Type Miscellaneous
  Year 2008 Publication (up) Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya Abbreviated Journal  
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  Corporate Author Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya Thesis  
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  ISSN ISBN Medium  
  Area Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya Expedition Conference  
  Notes MILAB;IAM;MV;SIAI Approved no  
  Call Number IAM @ iam @ VIM2008 Serial 1664  
Permanent link to this record
 

 
Author Petia Radeva edit  openurl
  Title On the Role of Intravascular Ultrasound Image Analysis Type Miscellaneous
  Year 2003 Publication (up) Angiography and Plaque Imaging: Advanced Segmentation Techniques, CRC, pp.397–450, ISBN: 0849317401 Abbreviated Journal  
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  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ Rad2003 Serial 402  
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Author J. Suri; S. Singh; S. Laxminarayan; R. Cesar; H. Jelinek; Petia Radeva; B. Roysam; Ch. Stewart; K. Frizche; J. Williams; H. Tek edit  openurl
  Title A Note on Future Research in Vascular and Plaque Segmentation Type Miscellaneous
  Year 2003 Publication (up) Angiography and Plaque Imaging: Advanced Segmentation Techniques, Editorial: CRC, pp.501–522, ISBN: 0849317401 Abbreviated Journal  
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  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ SSL2003 Serial 419  
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Author Jose Luis Alba; A. Pujol; Juan J. Villanueva edit  openurl
  Title Novel SOM-PCA Network for Face Identification. Type Miscellaneous
  Year 2001 Publication (up) Applications and Science of Neural Networks, Fuzzy Systems and Evolutonary Computation IV, SPIE´s International Symposium on Optical Science and Technology. Abbreviated Journal  
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  Notes Approved no  
  Call Number ISE @ ise @ APV2001a Serial 69  
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Author Daniel Ponsa; Xavier Roca edit  openurl
  Title A Novel Approach to Generate Multiple Shape Models. Type Miscellaneous
  Year 2002 Publication (up) Articulated Motion and Deformable Objects, LNCS 2492: 80–91, Springer Verlag. Abbreviated Journal  
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  Notes ADAS;ISE Approved no  
  Call Number ADAS @ adas @ PoR2002a Serial 283  
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Author Jordi Gonzalez; Javier Varona; Xavier Roca; Juan J. Villanueva edit  openurl
  Title Analysis of Human Walking Based on aSpaces Type Miscellaneous
  Year 2004 Publication (up) Articulated Motion and Deformable Objects, Third International Workshop, (AMDO 2004), Lecture Notes in Computer Science, F.J. Perales, B.A. Draper (Eds.), 3179:177–188 Abbreviated Journal  
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  Address Springer-Verlag, Berlin, Heidelberg  
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  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number ISE @ ise @ GVR2004a Serial 492  
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Author Adriana Romero; Petia Radeva; Carlo Gatta edit   pdf
openurl 
  Title No more meta-parameter tuning in unsupervised sparse feature learning Type Miscellaneous
  Year 2014 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
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  Abstract CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
 
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  Notes MILAB; LAMP; 600.079 Approved no  
  Call Number Admin @ si @ RRG2014 Serial 2471  
Permanent link to this record
 

 
Author Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang edit  openurl
  Title An Effective Solution to Double Counting Problem in Human Pose Estimation Type Miscellaneous
  Year 2015 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Pose estimation; double counting problem; mix-ture of parts Model  
  Abstract The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.

An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015].
 
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  Area Expedition Conference  
  Notes ISE; 600.078 Approved no  
  Call Number Admin @ si @ GHG2015 Serial 2590  
Permanent link to this record
 

 
Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva edit  openurl
  Title Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos Type Miscellaneous
  Year 2015 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness.  
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  Notes MILAB Approved no  
  Call Number Admin @ si @ ADR2015b Serial 2713  
Permanent link to this record
 

 
Author Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez edit   pdf
openurl 
  Title Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest Type Miscellaneous
  Year 2016 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Domain Adaptation; Pedestrian detection; Random Forest  
  Abstract Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ MVJ2016 Serial 2868  
Permanent link to this record
 

 
Author Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven edit   pdf
doi  openurl
  Title End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks Type Miscellaneous
  Year 2017 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
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  Abstract arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.
 
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  Notes HuPBA; ISE; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ GGM2017 Serial 2932  
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