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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, Oriol Rodriguez-Leor, Carme Julia, David Rotger, 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, Oriol Rodriguez-Leor, Carme 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, Oriol Rodriguez-Leor, Carme 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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David Rotger, Petia Radeva, E Fernandez-Nofrerias, & J. Mauri. (2002). Multimodal Registration of Intravascular Ultrasound Images and Angiography..
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David Rotger, Petia Radeva, J. Mauri, & E Fernandez-Nofrerias. (2002). Internal and External Coronary Vessel Images Registration..
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Francesc Tous, Agnes Borras, Robert Benavente, Ramon Baldrich, Maria Vanrell, & Josep Llados. (2002). Textual Descriptions for Browsing People by Visual Apperance. In Lecture Notes in Artificial Intelligence (Vol. 2504, pp. 419–429). Springer Verlag.
Abstract: This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building
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David Lloret. (2002). Medical Image Registration Based on a Creaseress Measure. (Joan Serrat, Ed.). Ph.D. thesis, , .
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