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Author Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin edit  openurl
  Title Subclass Problem-Dependent Design for Error-Correcting Output Codes Type Journal
  Year 2008 Publication (up) IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(6):1041–1054 Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ ETP2008 Serial 951  
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Author Adriana Romero; Carlo Gatta; Gustavo Camps-Valls edit   pdf
doi  openurl
  Title Unsupervised Deep Feature Extraction for Remote Sensing Image Classification Type Journal Article
  Year 2016 Publication (up) IEEE Transaction on Geoscience and Remote Sensing Abbreviated Journal TGRS  
  Volume 54 Issue 3 Pages 1349 - 1362  
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  Abstract This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.  
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  ISSN 0196-2892 ISBN Medium  
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  Notes LAMP; 600.079;MILAB Approved no  
  Call Number Admin @ si @ RGC2016 Serial 2723  
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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 (up) IEEE Transactions on Biomedical Engineering Abbreviated Journal TBME  
  Volume 59 Issue 4 Pages 1022-2031  
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  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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  ISSN 0018-9294 ISBN Medium  
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  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ ABG2012 Serial 1996  
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Author Jose Seabra; Francesco Ciompi; Oriol Pujol; Josepa Mauri; Petia Radeva; Joao Sanchez edit  doi
openurl 
  Title Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound Type Journal Article
  Year 2011 Publication (up) IEEE Transactions on Biomedical Engineering Abbreviated Journal TBME  
  Volume 58 Issue 5 Pages 1314-1324  
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  Abstract Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.  
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  Notes MILAB;HuPBA Approved no  
  Call Number Admin @ si @ SCP2011 Serial 1712  
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Author Marc Bolaños; Mariella Dimiccoli; Petia Radeva edit   pdf
doi  openurl
  Title Towards Storytelling from Visual Lifelogging: An Overview Type Journal Article
  Year 2017 Publication (up) IEEE Transactions on Human-Machine Systems Abbreviated Journal THMS  
  Volume 47 Issue 1 Pages 77 - 90  
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  Abstract Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging.
 
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  Notes MILAB; 601.235 Approved no  
  Call Number Admin @ si @ BDR2017 Serial 2712  
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