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Petia Radeva; Joan Serrat; Enric Marti A snake for model-based segmentation 1995 Proc. Conf. Fifth Int Computer Vision 816-821 details   pdf doi
Petia Radeva; Enric Marti An improved model of snakes for model-based segmentation 1995 Proceedings of Computer Analysis of Images and Patterns 515-520 details   pdf doi
Petia Radeva; Enric Marti Facial Features Segmentation by Model-Based Snakes 1995 International Conference on Computing Analysis and Image Processing details   url
Petia Radeva; A.Amini; J.Huang; Enric Marti Deformable B-Solids and Implicit Snakes for Localization and Tracking of SPAMM MRI-Data 1996 Workshop on Mathematical Methods in Biomedical Image Analysis 192-201 details   pdf doi
Paula Fritzsche; C.Roig; Ana Ripoll; Emilio Luque; Aura Hernandez-Sabate A Performance Prediction Methodology for Data-dependent Parallel Applications 2006 Proceedings of the IEEE International Conference on Cluster Computing 1-8 details   pdf doi
Pau Cano; Debora Gil; Eva Musulen Towards automatic detection of helicobacter pylori in histological samples of gastric tissue 2023 IEEE International Symposium on Biomedical Imaging details   openurl
Patricia Marquez;Debora Gil;Aura Hernandez-Sabate A Complete Confidence Framework for Optical Flow 2012 12th European Conference on Computer Vision – Workshops and Demonstrations 7584 124-133 details   pdf doi
Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging 2014 17th International Conference on Medical Image Computing and Computer Assisted Intervention 8896 231-238 details   pdf doi
Patricia Marquez; Debora Gil; R.Mester; Aura Hernandez-Sabate Local Analysis of Confidence Measures for Optical Flow Quality Evaluation 2014 9th International Conference on Computer Vision Theory and Applications 3 450-457 details   pdf openurl
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann When Is A Confidence Measure Good Enough? 2013 9th International Conference on Computer Vision Systems 7963 344-353 details   pdf doi
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