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Author Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title Texture Segmentation by Statistical Deformable Models Type Journal
  Year (up) 2004 Publication International Journal of Image and Graphics Abbreviated Journal IJIG  
  Volume 4 Issue 3 Pages 433-452  
  Keywords Texture segmentation, parametric active contours, statistic snakes  
  Abstract Deformable models have received much popularity due to their ability to include high-level knowledge on the application domain into low-level image processing. Still, most proposed active contour models do not sufficiently profit from the application information and they are too generalized, leading to non-optimal final results of segmentation, tracking or 3D reconstruction processes. In this paper we propose a new deformable model defined in a statistical framework to segment objects of natural scenes. We perform a supervised learning of local appearance of the textured objects and construct a feature space using a set of co-occurrence matrix measures. Linear Discriminant Analysis allows us to obtain an optimal reduced feature space where a mixture model is applied to construct a likelihood map. Instead of using a heuristic potential field, our active model is deformed on a regularized version of the likelihood map in order to segment objects characterized by the same texture pattern. Different tests on synthetic images, natural scene and medical images show the advantages of our statistic deformable model.  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PuR2004a Serial 505  
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Author Oriol Pujol; Debora Gil; Petia Radeva edit   pdf
doi  openurl
  Title Fundamentals of Stop and Go active models Type Journal Article
  Year (up) 2005 Publication Image and Vision Computing Abbreviated Journal  
  Volume 23 Issue 8 Pages 681-691  
  Keywords Deformable models; Geodesic snakes; Region-based segmentation  
  Abstract An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation.  
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  Publisher Butterworth-Heinemann Place of Publication Newton, MA, USA Editor  
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  ISSN 0262-8856 ISBN Medium  
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  Notes IAM;MILAB;HuPBA Approved no  
  Call Number IAM @ iam @ PGR2005 Serial 1629  
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Author Oriol Pujol; Petia Radeva; Jordi Vitria edit  openurl
  Title Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes Type Journal
  Year (up) 2006 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012 Abbreviated Journal  
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  Notes OR;MILAB;HuPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ PRV2006a Serial 646  
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Author Xavier Otazu; Oriol Pujol edit  doi
openurl 
  Title Wavelet based approach to cluster analysis. Application on low dimensional data sets Type Journal Article
  Year (up) 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 Sergio Escalera; Oriol Pujol; Petia Radeva edit  openurl
  Title Boosted Landmarks of Contextual Descriptors and Forest-ECOC: a Novel Framework to Detect and Classify Objects in Cluttered Scenes Type Journal
  Year (up) 2007 Publication Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2007c Serial 907  
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