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J.M. Sanchez, X. Binefa, & J.R. Kender. (2002). Multiple Feature Temporal Models for Object Detection in Video..
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Ricardo Toledo, Ramon Baldrich, Ernest Valveny, & Petia Radeva. (2002). Enhancing snakes for vessel detection in angiography images..
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Ernest Valveny, & B. Lamiroy. (2002). Automatic Generation of Browsable Technical Documents..
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Juan J. Villanueva, Jordi Gonzalez, X. Varona, & Xavier Roca. (2002). Aspaces: Action Spaces for Recognition and Synthesis of Human Actions..
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M. Bressan, & Jordi Vitria. (2002). Improving Naive Bayes using Class Condicitonal ICA..
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Cristina Cañero, E Fernandez-Nofrerias, J. Mauri, & Petia Radeva. (2002). Modelling the Acquisition Geometry of a C-arm Angiography System for 3D Reconstruction..
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M.Gomez, J.Mauri, E.Fernandez-Nofrerias, O.Rodriguez-Leor,, C.Julia, et al. (2002). Una nova aplicacio informatica per a la correlacio d imatges angiografiques i d ecografia intracoronaria. Revista de la Societat Catalana de Cardiologia, 4(4): 42, XIV Congres de la Societat Catalana de Cardiologia..
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M.Gomez, J.Mauri, E.Fernandez-Nofrerias, O. Rodriguez-Leor, C Julia, Misael Rosales, et al. (2002). Modelo fisico para la simulacion de ultrasonido Intravascular. XXXVIII Congreso Nacional de la Sociedad Española de Cardiologia..
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M.Gomez, J.Mauri, E.Fernandez-Nofrerias, O.Rodriguez-Leor, C.Julia, Petia Radeva, et al. (2002). Nuevos Avances para la correlacion de imagenes angiograficas y de ecograia intracoronaria..
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David Vazquez, & Antonio Lopez. (2008). Intrusion Classification in Intelligent Video Surveillance Systems.
Abstract: An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system
Keywords: Human detection; Car detection; Intrusion detection
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David Guillamet, & Jordi Vitria. (2002). Classifying Faces with Non-negative Matrix Factorization..
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Daniel Ponsa, & Xavier Roca. (2002). Unsupervised Parameterisation of Gaussian Mixture Models.
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Oriol Pujol, Petia Radeva, J. Mauri, & E Fernandez-Nofrerias. (2002). Automatic segmentation of lumen in Intravascular Ultrasound Images: An evaluation of texture feature extractors..
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Oriol Pujol, & Petia Radeva. (2002). Lumen Detection in Ivus Image Using Snakes in a Statical Framework..
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Petia Radeva, M. Bressan, A. Tovar, & Jordi Vitria. (2002). Bayesian Classification for Inspection of Industrial Products..
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