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Author Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo edit  doi
openurl 
  Title (up) 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  
  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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  Notes MILAB; HuPBA Approved no  
  Call Number Admin @ si @ EPA2011 Serial 1881  
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Author Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva edit  doi
openurl 
  Title (up) Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes Type Journal Article
  Year 2009 Publication Journal of Signal Processing Systems Abbreviated Journal  
  Volume 55 Issue 1-3 Pages 35–47  
  Keywords  
  Abstract Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.  
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  Series Volume Series Issue Edition  
  ISSN 1939-8018 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPM2009 Serial 1258  
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Author O. Rodriguez-Leor; E Fernandez-Nofrerias; J. Mauri; C. Garcia; R. Villuendas; V. Valle; Oriol Pujol; Petia Radeva edit  openurl
  Title (up) Intravascular ultrasound segmentation using local binary patterns Type Journal
  Year 2003 Publication European Heart Journal (IF: 5.997), ESC Congress 2003 Abbreviated Journal  
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  Address Vienna (Austria)  
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  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ RFM2003a Serial 407  
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Author Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund edit   pdf
url  openurl
  Title (up) Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams Type Journal Article
  Year 2016 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 75 Issue 22 Pages 14985-14990  
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  Notes ISE; HUPBA Approved no  
  Call Number Admin @ si @ DDB2016 Serial 2934  
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Author Frederic Sampedro; Sergio Escalera; Anna Puig edit  doi
openurl 
  Title (up) Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation Type Journal Article
  Year 2014 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 46 Issue Pages 1-10  
  Keywords Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation  
  Abstract In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios.  
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  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ SEP2014 Serial 2550  
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