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Author Cristina Cañero; Fernando Vilariño; Petia Radeva edit  openurl
  Title Predictive (un) distortion model and 3D Reconstruction by Biplane Snakes Type Journal
  Year 2002 Publication IEEE Transactions on Medical Imaging (IF: 2.911) Abbreviated Journal  
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  Notes MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ CVR2002 Serial 269  
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Author Cristina Cañero; Petia Radeva edit  doi
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  Title 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 Xose M. Pardo; Petia Radeva; D. Cabello edit  openurl
  Title Discriminant Snakes for 3D Reconstruction of Anatomical Organs Type Journal
  Year 2003 Publication Medical Image Analysis, 7(3): 293–310 (IF: 4.442) Abbreviated Journal  
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  Notes MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ PPC2003 Serial 398  
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Author Oriol Pujol; Petia Radeva edit  openurl
  Title Texture Segmentation by Statistic Deformable Models Type Journal
  Year 2003 Publication International Journal of Image and Graphics (IJIG) Abbreviated Journal  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PuR2003 Serial 432  
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Author Debora Gil; Petia Radeva edit   pdf
doi  openurl
  Title A Regularized Curvature Flow Designed for a Selective Shape Restoration Type Journal Article
  Year 2004 Publication IEEE Transactions on Image Processing Abbreviated Journal  
  Volume 13 Issue Pages 1444–1458  
  Keywords Geometric flows, nonlinear filtering, shape recovery.  
  Abstract Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization.  
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  Notes IAM;MILAB Approved no  
  Call Number BCNPCL @ bcnpcl @ GiR2004b Serial 491  
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