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Author Sergio Escalera edit   pdf
url  openurl
  Title Multi-Modal Human Behaviour Analysis from Visual Data Sources Type Journal
  Year 2013 Publication ERCIM News journal Abbreviated Journal ERCIM  
  Volume 95 Issue Pages 21-22  
  Keywords (up)  
  Abstract The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-4981 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ Esc2013 Serial 2361  
Permanent link to this record
 

 
Author Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo edit  doi
openurl 
  Title Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes Type Journal Article
  Year 2011 Publication Computer Graphics Forum Abbreviated Journal CGF  
  Volume 30 Issue 7 Pages 2107-2115  
  Keywords (up)  
  Abstract IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.
 
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; HuPBA Approved no  
  Call Number Admin @ si @ EPA2011 Serial 1881  
Permanent link to this record
 

 
Author Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu edit   pdf
doi  openurl
  Title Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction Type Journal Article
  Year 2012 Publication Sensors Abbreviated Journal SENS  
  Volume 12 Issue 2 Pages 1702-1719  
  Keywords (up)  
  Abstract IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
 
  Address  
  Corporate Author Thesis  
  Publisher Molecular Diversity Preservation International Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; OR;HuPBA;MV Approved no  
  Call Number Admin @ si @ EBV2012 Serial 1885  
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Author Marina Alberti; Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Joana Silva; Xavier Carrillo; Petia Radeva edit  url
doi  openurl
  Title Automatic Bifurcation Detection in Coronary IVUS Sequences Type Journal Article
  Year 2012 Publication IEEE Transactions on Biomedical Engineering Abbreviated Journal TBME  
  Volume 59 Issue 4 Pages 1022-2031  
  Keywords (up)  
  Abstract In this paper, we present a fully automatic method which identifies every bifurcation in an intravascular ultrasound (IVUS) sequence, the corresponding frames, the angular orientation with respect to the IVUS acquisition, and the extension. This goal is reached using a two-level classification scheme: first, a classifier is applied to a set of textural features extracted from each image of a sequence. A comparison among three state-of-the-art discriminative classifiers (AdaBoost, random forest, and support vector machine) is performed to identify the most suitable method for the branching detection task. Second, the results are improved by exploiting contextual information using a multiscale stacked sequential learning scheme. The results are then successively refined using a-priori information about branching dimensions and geometry. The proposed approach provides a robust tool for the quick review of pullback sequences, facilitating the evaluation of the lesion at bifurcation sites. The proposed method reaches an F-Measure score of 86.35%, while the F-Measure scores for inter- and intraobserver variability are 71.63% and 76.18%, respectively. The obtained results are positive. Especially, considering the branching detection task is very challenging, due to high variability in bifurcation dimensions and appearance.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0018-9294 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ ABG2012 Serial 1996  
Permanent link to this record
 

 
Author Miguel Angel Bautista; Sergio Escalera; Oriol Pujol edit   pdf
doi  openurl
  Title On the Design of an ECOC-Compliant Genetic Algorithm Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 2 Pages 865-884  
  Keywords (up)  
  Abstract Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ BEP2013 Serial 2254  
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