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Author Xavier Otazu; Oriol Pujol edit  doi
openurl 
  Title (down) Wavelet based approach to cluster analysis. Application on low dimensional data sets Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue 14 Pages 1590–1605  
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  Notes MILAB; CIC; HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ OtP2006 Serial 658  
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Author Cristina Cañero; Petia Radeva edit  doi
openurl 
  Title (down) Vesselness enhancement diffusion Type Journal Article
  Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 24 Issue 16 Pages 3141–3151  
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  Abstract IF: 0.809  
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  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ CaR2003 Serial 371  
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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 (down) 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; Josefina Mauri;Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva edit  openurl
  Title (down) 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 Adriana Romero; Carlo Gatta; Gustavo Camps-Valls edit   pdf
doi  openurl
  Title (down) Unsupervised Deep Feature Extraction for Remote Sensing Image Classification Type Journal Article
  Year 2016 Publication 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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