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Petia Radeva, & Joan Serrat. (1993). Rubber Snake: Implementation on Signed Distance Potential. In Vision Conference (pp. 187–194).
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Petia Radeva, Joan Serrat, & Enric Marti. (1995). A snake for model-based segmentation. In Proc. Conf. Fifth Int Computer Vision (pp. 816–821).
Abstract: Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signi cant improvement.
Keywords: snakes; elastic matching; model-based segmenta tion
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Petia Radeva, & Jordi Vitria. (2001). Region Based Approach for Discriminant Snakes..
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Petia Radeva, & Jordi Vitria. (2003). “Inteligencia artificial” Centre de Visio per Computador.
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Petia Radeva, & Jordi Vitria. (2001). Region-Based Approach for Discriminant Snakes.
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Petia Radeva, & Jordi Vitria. (2004). Discriminant Projections Embedding for Nearest Neighbor Classification.
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Petia Radeva, & Jordi Vitria. (2004). Corkinspect: Statistical Learning of Natural Material. Italian Beverage Technology, 13(38):11–18.
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Petia Radeva, Jordi Vitria, Fernando Vilariño, Panagiota Spyridonos, Fernando Azpiroz, Juan Malagelada, et al. (2009). Cascade analysis for intestinal contraction detection. US Patent Office.
Abstract: 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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Petia Radeva, Judit Martinez, A. Tovar, X. Binefa, Jordi Vitria, & Juan J. Villanueva. (1999). CORKIDENT: an automatic vision system for real-time inspection of natural products.
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Petia Radeva, M. Bressan, A. Tovar, & Jordi Vitria. (2002). Bayesian Classification for Inspection of Industrial Products..
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Petia Radeva, M. Bressan, A. Tovar, & Jordi Vitria. (2002). Real-Time Inspection of cork stoppers using parametric methods in high dimensional spaces..
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Petia Radeva, M. Bressan, A. Tovar, & Jordi Vitria. (2002). Bayesian Classification for Inspection of Industrial Products..
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Petia Radeva, & M. Scoccianti. (2000). 3D Reconstruction of Abdominal Aortic Aneurysm.
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Petia Radeva, Maya Dimitrova, Ch. Roumenin, David Rotger, D. Nikolov, & Juan J. Villanueva. (2004). Integration of Multiple Sensor Modalities in ActiveVessel Cardiology Workstation.
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Petia Radeva, Michal Drozdzal, Santiago Segui, Laura Igual, Carolina Malagelada, Fernando Azpiroz, et al. (2012). Active labeling: Application to wireless endoscopy analysis. In High Performance Computing and Simulation, International Conference on (pp. 174–181).
Abstract: 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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