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Philippe Dosch; Josep Llados |
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Title |
Vectorial Signatures for Symbol Discrimination |
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Miscellaneous |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:150–161, ISBN: 3–540–22478–5 |
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DAG @ dag @ DoL2004 |
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461 |
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Author |
Philippe Dosch; Ernest Valveny |
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Title |
Report on the Second Symbol Recognition Contest |
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Book Chapter |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397 |
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DAG @ dag @ DoV2006 |
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691 |
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Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
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Title |
3D Vessel Reconstruction from Biplane Angiograms using Snakes. |
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1998 |
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Cleveland, OH |
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MILAB;ADAS |
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BCNPCL @ bcnpcl @ RTV1998a |
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198 |
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Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
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Title |
3D Dynamic Model of the Coronary Tree. |
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1998 |
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Cleveland, OH |
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MILAB;ADAS |
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BCNPCL @ bcnpcl @ RTV1998b |
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200 |
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Author |
Petia Radeva; Michal Drozdzal; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
Active labeling: Application to wireless endoscopy analysis |
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Conference Article |
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2012 |
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High Performance Computing and Simulation, International Conference on |
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174-181 |
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Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”. |
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978-1-4673-2359-8 |
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HPCS |
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MILAB; OR;MV |
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Admin @ si @ RDS2012 |
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2152 |
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Author |
Petia Radeva; Maya Dimitrova; Ch. Roumenin; David Rotger; D. Nikolov; Juan J. Villanueva |
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Title |
Integration of Multiple Sensor Modalities in ActiveVessel Cardiology Workstation |
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2004 |
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Varna (Bulgaria) |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RDR2004 |
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473 |
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Author |
Petia Radeva; M. Scoccianti |
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Title |
3D Reconstruction of Abdominal Aortic Aneurysm |
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Miscellaneous |
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2000 |
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Elsevier Science B.V., Ed. H.U. Lemke, M.W. Vannier, K. Inamura, A.G.Farman and K.Doi, CARS 2000, pp.1014, ISBN:0–444–50536–9 |
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San Francisco, USA |
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no |
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BCNPCL @ bcnpcl @ RaS2000 |
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439 |
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Author |
Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Title |
Bayesian Classification for Inspection of Industrial Products. |
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2002 |
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OR;MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ RBT2002a |
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285 |
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Author |
Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Title |
Real-Time Inspection of cork stoppers using parametric methods in high dimensional spaces. |
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Miscellaneous |
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2002 |
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Proceedings of the Fourth IASTED International Conference, Signal and Image Processing: 480–484. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RBT2002b |
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296 |
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Author |
Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Title |
Bayesian Classification for Inspection of Industrial Products. |
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2002 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RBT2002c |
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316 |
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Author |
Petia Radeva; Judit Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva |
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Title |
CORKIDENT: an automatic vision system for real-time inspection of natural products. |
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1999 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RMT1999 |
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23 |
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Petia Radeva; Jordi Vitria; Fernando Vilariño; Panagiota Spyridonos; Fernando Azpiroz; Juan Malagelada; Fosca de Iorio; Anna Accarino |
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Title |
Cascade analysis for intestinal contraction detection |
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2009 |
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US 2009/0284589 A1 |
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USPO |
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1-25 |
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A method and system cascade analysisi for intestinal contraction detection is provided by extracting from image frames captured in-vivo. The method and system also relate to the detection of turbid liquids in intestinal tracts, to automatic detection of video image frames taken in the gastrointestinal tract including a field of view obstructed by turbid media, and more particulary, to extraction of image data obstructed by turbid media. |
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US Patent Office |
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US Patent Office |
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IAM @ iam @ RVV2009 |
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1700 |
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Author |
Petia Radeva; Jordi Vitria |
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Title |
Region Based Approach for Discriminant Snakes. |
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2001 |
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Proceedings IEEE International Conference on Image Processing, 801–804. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RVi2001 |
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160 |
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Petia Radeva; Jordi Vitria |
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“Inteligencia artificial” Centre de Visio per Computador |
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2003 |
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Europacork.com (Digital Journal) Any II num. 7 |
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BCNPCL @ bcnpcl @ RaV2003b |
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404 |
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Petia Radeva; Jordi Vitria |
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Region-Based Approach for Discriminant Snakes |
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2001 |
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Proceedings of ICIP |
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Thesaloniki, Greece |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RaV2001 |
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436 |
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