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X. Binefa, Jordi Vitria, & Xavier Roca. (1993). Deteccion de profundidad en imagenes monoculares mediante vision activa. Revista de Optica Pura y Aplicada, 26(3), 636–648.
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Xavier Roca, & Jordi Vitria. (1993). Multiscale Structure Extraction using Morphological Tools. Applications to Edge Detection. In SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV)..
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Xavier Roca, X. Binefa, & Jordi Vitria. (1993). Multiscale Structure Extraction using Morphological Tools. Applications to Edge Detection and to Depth Perception. In Technical Workshop on Mathematical Morphology and its Applications to Signal Processing..
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X. Binefa, Jordi Vitria, & Xavier Roca. (1992). Deteccion de profundidad en imagenes monoculares mediante vision activa. In I Reunion Iberoamericana de Optica.
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Visual behaviours for binocular navigation with autonomous systems..
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1997). A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Machine Vision and Applications, 9, 262–271.
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Gaze control in a binocular robot systems. In 7th IEEE International Conference on Emerging Technologies and Factory Automation. Proceedings ETFA '99.
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1993). A General Morphological Framework for Perceptual Texture Discrimination based on Granulometries. In Technical Workshop on Mathematical Morphology and its Applications to Signal Processing..
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (2000). Visual behaviours for binocular navigation with autonomous systems..
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F. Javier Sanchez, Jordi Vitria, & Enric Marti. (1991). Transformaciones Morfológicas de Polígonos Isotéticos. In Primer Congreso Español de Informática Gráfica..
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Oriol Pujol, & David Masip. (2009). Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6), 1140–1146.
Abstract: This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention
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Laura Igual, & Xavier Baro. (2013). Experiencia de aprendizaje de programación basada en proyectos. Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación.
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Xavier Baro. (2005). Fast traffic sign detection on gray-scale images.
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Xavier Baro, & Jordi Vitria. (2005). Feature Selection with Non-Parametric Mutual Information for Adaboost Learning.
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Xavier Baro, & Jordi Vitria. (2005). Feature Selection with Non-Parametric Mutual Information for Adaboost Learning. In Frontiers in Artificial Intelligence and Applications / Artificial intelligence Research and Development, 131:131–138, Eds: B. Lopez, J. Melendez, P. Radeva, J. Vitria, IOS Press, ISBN: 1–58603–560–6.
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