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Author (up) Oriol Pujol; Debora Gil; Petia Radeva edit   pdf
doi  openurl
  Title Fundamentals of Stop and Go active models Type Journal Article
  Year 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.  
  Address  
  Corporate Author Thesis  
  Publisher Butterworth-Heinemann Place of Publication Newton, MA, USA Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes IAM;MILAB;HuPBA Approved no  
  Call Number IAM @ iam @ PGR2005 Serial 1629  
Permanent link to this record
 

 
Author (up) Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
doi  openurl
  Title A Flexible Outlier Detector Based on a Topology Given by Graph Communities Type Journal Article
  Year 2022 Publication Big Data Research Abbreviated Journal BDR  
  Volume 29 Issue Pages 100332  
  Keywords Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors  
  Abstract Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings.
 
  Address August 28, 2022  
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  Notes DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 Approved no  
  Call Number Admin @ si @ RBG2022a Serial 3718  
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Author (up) Oriol Rodriguez-Leor; A. Carol; H. Tizon; Eduard Fernandez-Nofrerias; Josefina Mauri; Vicente del Valle; Debora Gil; Aura Hernandez-Sabate; Petia Radeva edit  openurl
  Title Model estadístic-determinístic per la segmentació de l adventicia en imatges d ecografía intracoronaria Type Journal Article
  Year 2005 Publication Rev Societat Catalana Cardiologia Abbreviated Journal  
  Volume 5 Issue Pages 41  
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  Notes IAM;MILAB Approved no  
  Call Number IAM @ iam @ RCT2005 Serial 1637  
Permanent link to this record
 

 
Author (up) Oriol Rodriguez-Leor; Debora Gil; Eduard Fernandez-Nofrerias edit  openurl
  Title Analisis en los cambios en el nivel de gris en las secuencias angiograficas mediante descriptores estadisticos para determinar la perfusion miocardica Type Journal Article
  Year 2006 Publication Revista Española de Cardiología Abbreviated Journal REC  
  Volume 59 Supl 2-166 Issue 2 Pages 128  
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  Notes IAM; Approved no  
  Call Number IAM @ iam @ RGF2006 Serial 1640  
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Author (up) Oriol Rodriguez-Leor; Josefina Mauri; Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva edit  openurl
  Title 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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  Area Expedition Conference SEC  
  Notes MILAB;IAM Approved no  
  Call Number BCNPCL @ bcnpcl @ RMF2004 Serial 566  
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